Apparatus and methods for identifying abnormal biomedical features within images of biomedical data

Abstract

An apparatus for tracking cardiac indices containing a processor configured to receive cardiac input data from a patient including a plurality of cardiac signals, input the cardiac input data into a cardiac panel, the cardiac panel including a plurality of cardiac models, and generate one or more cardiac indices from the cardiac panel as a function of the cardiac input data and the at least one cardiac machine learning model, wherein at least one cardiac index of the one or more cardiac indices includes a probability of the patient satisfying at least one grading threshold.

Description

[0001]APPARATUS AND METHODS FOR IDENTIFYING ABNORMAL BIOMEDICAL FEATURES WITHIN IMAGES OF BIOMEDICAL DATA CROSS-REFERENCE TO RELATED APPLICATIONS This application is an International PCT Application of U.S. Non-provisional Application Serial No.18 / 771,914, filed on July 12, 2024, and entitled “APPARATUS AND METHODS FOR IDENTIFYING ABNORMAL BIOMEDICAL FEATURES WITHIN IMAGES OF BIOMEDICAL DATA,” and U.S. Non-provisional Application Serial No.18 / 771,678, filed on July 12, 2024, and entitled “APPARATUS AND METHOD FOR DETECTING HYPERTENSION ATTRIBUTES,” and U.S. Non-provisional Application Serial No. 18 / 771,472, filed on July 12, 2024, and entitled “APPARATUS AND A METHOD FOR IDENTIFYING THE PROGRESSION OF CORONARY HEART DISEASE,” and U.S. Non- provisional Application Serial No.18 / 773,302, filed on July 15, 2024, and entitled “APPARATUS AND METHOD FOR DETERMINING WOMEN’S HEALTH ATTRIBUTES IN FEMALE CLASSIFICATION TIME-SERIES DATA,” and U.S. Non-provisional Application Serial No.18 / 773,195, filed on July 15, 2024, and entitled “APPARATUS AND METHOD FOR GENERATING A PREOPERATIVE DATA STRUCTURE USING A PRE- OPERATIVE PANEL,” each of which are incorporated herein by reference in their entirety. FIELD OF THE INVENTION The present invention generally relates to the field of biomedical data analysis. In particular, the present invention is directed to apparatus and methods for tracking cardiac indices related to diastolic dysfunction. BACKGROUND Early detection of medically relevant features in biomedical data plays a crucial role in the timely diagnosis of many challenging medical conditions such as systolic and diastolic dysfunction, yet individual detection models are often limited in their accuracy for detection of such medically relevant features. Additionally, tracking the temporal evolution of a medically relevant feature using time series data often reveals useful information regarding a patient’s journey through a medical condition. However, medical professionals are often equipped with one detection model at a time and presented with a large quantity of time series data that are difficult to analyze, which hinders the efficiency of making diagnostic decisions. 1 Attorney Docket No.1518-126PCT1 SUMMARY OF THE DISCLOSURE In an aspect, an apparatus for tracking cardiac indices is described. The apparatus includes a processor, and a memory communicatively connected to the processor, wherein the memory contains instructions configurating the processor to receive cardiac input data from a patient including a plurality of cardiac signals, input the cardiac input data into a cardiac panel, the cardiac panel including a plurality of cardiac models, wherein at least one cardiac model of the cardiac panel is configured to calculate a cardiac index associated with a heart condition, the at least one cardiac model includes at least one cardiac machine learning model that has been trained and configured to receive cardiac input data as inputs and output cardiac indices, and the at least one cardiac model is configured to calculate a cardiac index associated with diastolic dysfunction, and generate one or more cardiac indices from the cardiac panel as a function of the cardiac input data and the at least one cardiac machine learning model, wherein at least one cardiac index of the one or more cardiac indices includes a probability of the patient satisfying at least one grading threshold. In another aspect, the cardiac input data includes at least one electrocardiogram (ECG). In another aspect, training the at least one cardiac machine learning model includes receiving a plurality of cardiac training data associated with a plurality of patients, pretraining the at least one cardiac machine learning model as a function of the plurality of cardiac training data by adjusting one or more parameters within the at least one cardiac machine learning model, and retraining the at least one cardiac machine learning model as a function of the one or more parameters and a labeled subset of the plurality of cardiac training data. In another aspect, the at least one cardiac model includes a classification model that classifies a case of diastolic dysfunction under one category of a plurality of categories. In another aspect, at least one cardiac index of the one or more cardiac indices includes an elevated left ventricular filling pressure, and the at least one grading threshold includes a grading threshold in elevated left ventricular filling pressure. In another aspect, at least one cardiac index of the one or more cardiac indices is associated with pulmonary hypertension, and the at least one grading threshold includes a grading threshold associated with pulmonary hypertension. In another aspect, inputting the cardiac input data into the cardiac panel includes inputting a first plurality of time series data containing the cardiac input data, and generating the 2 Attorney Docket No.1518-126PCT1 one or more cardiac indices includes generating a second plurality of time series data containing the one or more cardiac indices. In another aspect, at least one of the one or more cardiac indices includes a cardiac index deviation. In another aspect, generating the one or more cardiac indices includes receiving at least one user update, and updating at least one cardiac index of the one or more cardiac indices as a function of the at least one user update. In another aspect, generating the at least one cardiac index of the one or more cardiac indices includes comparing the one or more cardiac indices to one or more cardiac baselines, calculating one or more distance metrics as a function of the comparison, and generating the one or more cardiac indices as a function of at least one distance metric of the one or more distance metrics. In another aspect, the processor is further configured to display at least one cardiac index of the one or more cardiac indices through a user interface. In another aspect, displaying the at least one cardiac index of the one or more cardiac indices further includes generating a color-coded visualization as a function of the at least one cardiac index of the one or more cardiac indices and the at least one distance metric of the one or more distance metrics. In another aspect, the processor is further configured to predict a projected cardiac index as a function of the cardiac input data and the at least one cardiac machine learning model. In an aspect, a method for tracking cardiac indices is described. The method includes receiving, by a processor, cardiac input data from a patient including a plurality of cardiac signals, inputting, by the processor, the cardiac input data into a cardiac panel, the cardiac panel including a plurality of cardiac models, wherein at least one cardiac model of the cardiac panel is configured to calculate a cardiac index associated with a heart condition, the at least one cardiac model includes at least one cardiac machine learning model that has been trained and configured to receive cardiac input data as inputs and output cardiac indices, and the at least one cardiac model is configured to calculate a cardiac index associated with diastolic dysfunction, and generating, by the processor, one or more cardiac indices from the cardiac panel as a function of the cardiac input data and the at least one cardiac machine learning model, wherein at least one cardiac index of the one or more cardiac indices includes a probability of the patient satisfying at 3 Attorney Docket No.1518-126PCT1 least one grading threshold. In another aspect, the cardiac input data includes at least one electrocardiogram (ECG). In another aspect, training the at least one cardiac machine learning model includes receiving a plurality of cardiac training data associated with a plurality of patients, pretraining the at least one cardiac machine learning model as a function of the plurality of cardiac training data by adjusting one or more parameters within the at least one cardiac machine learning model, and retraining the at least one cardiac machine learning model as a function of the one or more parameters and a labeled subset of the plurality of cardiac training data. In another aspect, the at least one cardiac model includes a classification model that classifies a case of diastolic dysfunction under one category of a plurality of categories. In another aspect, at least one cardiac index of the one or more cardiac indices includes an elevated left ventricular filling pressure, and the at least one grading threshold includes a grading threshold in elevated left ventricular filling pressure. In another aspect, at least one cardiac index of the one or more cardiac indices is associated with pulmonary hypertension, and the at least one grading threshold includes a grading threshold associated with pulmonary hypertension. In another aspect, inputting the cardiac input data into the cardiac panel includes inputting a first plurality of time series data containing the cardiac input data, and generating the one or more cardiac indices includes generating a second plurality of time series data containing the one or more cardiac indices. In another aspect, at least one of the one or more cardiac indices includes a cardiac index deviation. In another aspect, generating the one or more cardiac indices includes receiving at least one user update, and updating at least one cardiac index of the one or more cardiac indices as a function of the at least one user update. In another aspect, generating the one or more cardiac indices includes comparing the one or more cardiac indices to one or more cardiac baselines, calculating one or more distance metrics as a function of the comparison, and generating the one or more cardiac indices as a function of at least one distance metric of the one or more distance metrics. In another aspect, the method further includes displaying, by the processor, at least one cardiac index of the one or more cardiac indices using a user interface. 4 Attorney Docket No.1518-126PCT1 In another aspect, displaying the at least one cardiac index of the one or more cardiac indices further includes generating a color-coded visualization as a function of the at least one cardiac index of the one or more cardiac indices and the at least one distance metric of the one or more distance metrics. In another aspect, the method further includes predicting, by the processor, a projected cardiac index as a function of the cardiac input data and the at least one cardiac machine learning model. In an aspect, an apparatus for detecting hypertension attributes in a patient time-series data is described. The apparatus includes at least a processor, and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive a patient time-series data associated with a patient, wherein the patient time-series data is captured using a measurement device, input the patient time-series data into a hypertension panel wherein the hypertension panel includes of a plurality of hypertension models, and generate a hypertension attribute from the hypertension panel as a function of the patient time-series data and the plurality of hypertension models, wherein generating the hypertension attribute includes generating, using a first hypertension model, a first hypertension attribute including a measurement of the patient yielding a first hypertension level using the patient time-series data, and generating, using a second hypertension model, a second hypertension attribute using the patient time-series data. In another aspect, the instructions further configure the at least a processor to generate a confidence score from the hypertension panel as a function of the patient time-series data and at least one of the first hypertension model and the second hypertension model. In another aspect, the first hypertension level includes a systolic blood pressure and a diastolic blood pressure. In another aspect, the second hypertension model includes one or more of a hypertension classifier model, a hypertension prediction model, and a hypertension correlation model. In another aspect, the second hypertension model includes a loss function. In another aspect, the instructions further configure to train the second hypertension model, wherein training the second hypertension model includes receiving a plurality of patient time-series data examples associated with a plurality of patients, pretraining the second hypertension model in the hypertension panel as a function of the plurality of patient time-series 5 Attorney Docket No.1518-126PCT1 data examples by adjusting one or more parameter attributes of the second hypertension model, and training the second hypertension model as a function of the one or more parameter attributes and an electronic health record. In another aspect, the hypertension attribute includes a hypertension deviation, wherein the hypertension deviation includes a change in the hypertension attribute. In another aspect, inputting the patient time-series data into the hypertension panel includes selecting the second hypertension model from a plurality of hypertension models as a function of an input and a graphical user interface. In another aspect, displaying the hypertension attribute includes comparing the hypertension attribute to a target blood pressure level, and displaying the hypertension attribute as a function of the comparison. In another aspect, displaying the hypertension attribute through a graphical user interface includes generating a visual element associated with the hypertension attribute, wherein the visual element is further associated with an event handler. In an aspect, a method for detecting hypertension attributes in a patient time-series data is described. The method includes receiving a patient time-series data associated with a patient, wherein the patient time-series data is captured using a measurement device, inputting the patient time-series data into a hypertension panel wherein the hypertension panel includes of a plurality of hypertension models, and generating a hypertension attribute from the hypertension panel as a function of the patient time-series data and the plurality of hypertension models, wherein generating the hypertension attribute includes generating, using a first hypertension model, a first hypertension attribute including a measurement of the patient yielding a first hypertension level using the patient time-series data, and generating, using a second hypertension model, a second hypertension attribute using the patient time-series data. In another aspect, the method further includes generating a confidence score from the hypertension panel as a function of the patient time-series data and at least one of the first hypertension model and the second hypertension model. In another aspect, the first hypertension level includes a systolic blood pressure and a diastolic blood pressure. In another aspect, the second hypertension model includes one or more of a hypertension classifier model, a hypertension prediction model, and a hypertension correlation model. 6 Attorney Docket No.1518-126PCT1 In another aspect, the second hypertension model includes a loss function. In another aspect, the method further includes training the second hypertension model, wherein training the second hypertension model includes receiving a plurality of patient time- series data examples associated with a plurality of patients, pretraining the second hypertension model in the hypertension panel as a function of the plurality of patient time-series data examples by adjusting one or more parameter attributes of the second hypertension model, and training the second hypertension model as a function of the one or more parameter attributes and an electronic health record. In another aspect, the hypertension attribute includes a hypertension deviation, wherein the hypertension deviation includes a change in the hypertension attribute. In another aspect, inputting the patient time-series data into the hypertension panel includes selecting the second hypertension model from a plurality of hypertension models as a function of an input and a graphical user interface. In another aspect, displaying the hypertension attribute includes comparing the hypertension attribute to a target blood pressure level, and displaying the hypertension attribute as a function of the comparison. In another aspect, displaying the hypertension attribute through a graphical user interface includes generating a visual element associated with the hypertension attribute, wherein the visual element is further associated with an event handler. In an aspect, an apparatus for identifying a progression of coronary heart disease, wherein the apparatus includes at least a processor, and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive a subject profile associated with a subject, wherein the subject profile includes a plurality of electrocardiogram (ECG) data, identify contextual data as a function of the subject profile, generate a set of cardiac scores as a function of the contextual data and the plurality of ECG data using a set of cardiac machine learning models, and select at least one stage of coronary heart disease from a plurality of stages of coronary heart disease as a function of the set of cardiac scores. In another aspect, the set of cardiac scores includes at least one cardiac score associated with each stage of coronary heart disease of the plurality of stages of coronary heart disease, and the set of cardiac machine learning models includes at least one cardiac machine learning model 7 Attorney Docket No.1518-126PCT1 associated with each stage of coronary heart disease of the plurality of stages of coronary heart disease. In another aspect, generating the set of cardiac scores includes iteratively training the set of cardiac machine learning models using cardiac training data, wherein the cardiac training data includes examples of ECG data and examples of contextual data as inputs correlated to examples of cardiac scores as outputs, and wherein iteratively training the set of cardiac machine learning models includes classifying the cardiac training data into a plurality of cardiac training subsets associated with the plurality of stages of coronary heart disease, and iteratively training each cardiac machine learning model of the set of cardiac machine learning models using each cardiac training subset of the plurality of cardiac training subsets, and generating the set of cardiac scores using the trained set of cardiac machine learning models. In another aspect, the plurality of stages of coronary heart disease includes a plurality of ranges of total plaque volume (TPV) associated with the subject. In another aspect, the plurality of stages of coronary heart disease includes a plurality of ranges of percent arterial volume (PAV) associated with the subject. In another aspect, the memory further instructs the at least a processor to generate an impact score as a function of the contextual data. In another aspect, iteratively training the set of cardiac machine learning models additionally includes calibrating each cardiac machine learning model of the set of cardiac machine learning models using the impact score. In another aspect, each cardiac score of the set of cardiac scores includes a confidence interval. In another aspect, selecting the at least one stage of coronary heart disease includes selecting the at least one stage of coronary heart disease from a plurality of stages of coronary heart disease as a function of the confidence interval of each cardiac score of the set of cardiac scores. In another aspect, receiving the subject profile includes receiving the subject profile from an electronic medical record. In an aspect, a method for identifying a progression of coronary heart disease, wherein the method includes receiving, using at least a processor, a subject profile associated with a subject, wherein the subject profile includes a plurality of electrocardiogram (ECG) data, 8 Attorney Docket No.1518-126PCT1 identifying, using the at least a processor, contextual data as a function of the subject profile, generating, using the at least a processor, a set of cardiac scores as a function of the contextual data and the plurality of ECG data using a set of cardiac machine learning models, and selecting, using the at least a processor, at least one stage of coronary heart disease from a plurality of stages of coronary heart disease as a function of the set of cardiac scores. In another aspect, the set of cardiac scores includes at least one cardiac score associated with each stage of coronary heart disease of the plurality of stages of coronary heart disease, and the set of cardiac machine learning models includes at least one cardiac machine learning model associated with each stage of coronary heart disease of the plurality of stages of coronary heart disease. In another aspect, generating the set of cardiac scores includes iteratively training the set of cardiac machine learning models using cardiac training data, wherein the cardiac training data includes examples of ECG data and examples of contextual data as inputs correlated to examples of cardiac scores as outputs, and generating the set of cardiac scores using the trained set of cardiac machine learning models, and iteratively training the set of cardiac machine learning models includes classifying the cardiac training data into a plurality of cardiac training subsets associated with the plurality of stages of coronary heart disease, and iteratively training each cardiac machine learning model of the set of cardiac machine learning models using each cardiac training subset of the plurality of cardiac training subsets. In another aspect, the plurality of stages of coronary heart disease includes a plurality of ranges of total plaque volume (TPV) associated with the subject. In another aspect, the plurality of stages of coronary heart disease includes a plurality of ranges of percent arterial volume (PAV) associated with the subject. In another aspect, the method further includes generating, using the at least a processor, an impact score as a function of the contextual data. In another aspect, iteratively training the set of cardiac machine learning models additionally includes calibrating each cardiac machine learning model of the set of cardiac machine learning models using the impact score. In another aspect, each cardiac score of the set of cardiac scores includes a confidence interval. In another aspect, selecting the at least one stage of coronary heart disease includes 9 Attorney Docket No.1518-126PCT1 selecting the at least one stage of coronary heart disease from a plurality of stages of coronary heart disease as a function of the confidence interval of each cardiac score of the set of cardiac scores. In another aspect, receiving the subject profile includes receiving the subject profile from an electronic medical record. In an aspect, an apparatus for determining women’s health attributes in time-series data is described. The apparatus includes at least a processor, and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive time series data associated with a female classification, input the time-series data into a women’s health panel wherein the women’s health panel includes a plurality of women’s health models, and generate a women’s health attribute from the women’s health panel as a function of the time-series data and a women’s health model, wherein generating the women’s health attribute includes generating, using a first women’s health model, a first women’s health attribute, and generating, using a second women’s health model, a second women’s health attribute. In another aspect, the time-series data includes electrocardiogram data. In another aspect, the apparatus further includes a measurement device, wherein the measurement device includes one or more transducers. In another aspect, the plurality of women’s health models includes a loss function, and the instructions further configure the at least a processor to generate a confidence score from the women’s health panel as a function of the time-series data and at least one of the first women’s health model and the second women’s health model. In another aspect, the plurality of women’s health models includes a women’s health classifier model, a women’s health prediction model, and a women’s health correlation model. In another aspect, training the women’s health model includes receiving a plurality of time-series data examples associated with the female classification, pretraining the women’s health model in the women’s health panel as a function of the plurality of time-series data examples by adjusting one or more parameter attributes of the women’s health model, and training the women’s health model as a function of the one or more parameter attributes and a database. In another aspect, the first women’s health attribute includes a peripartum 10 Attorney Docket No.1518-126PCT1 cardiomyopathy attribute, and the second women’s health attribute includes a coronary heart disease attribute. In another aspect, the women’s health attribute includes a women’s health deviation, wherein the women’s health deviation includes a change in the women’s health attribute. In another aspect, inputting the time-series data into the women’s health panel includes selecting the women’s health model from a plurality of women’s health models as a function of an input and a graphical user interface. In another aspect, displaying the women’s health attribute includes comparing the women’s health attribute to a nominal women’s health attribute, and displaying the women’s health attribute as a function of the comparison. In an aspect, a method for determining women’s health attributes in time-series data is described. The method includes receiving time-series data associated with a female classification, inputting the time-series data into a women’s health panel wherein the women’s health panel includes of a plurality of women’s health models, and generating a women’s health attribute from the women’s health panel as a function of the time-series data and a women’s health model, wherein generating the women’s health attribute includes generating, using a first women’s health model, a first women’s health attribute, and generating, using a second women’s health model, a second women’s health attribute including. In another aspect, the time-series data includes electrocardiogram data. In another aspect, the method further includes a measurement device, wherein the measurement device includes one or more transducers. In another aspect, the plurality of women’s health models includes a loss function, and the method further includes generating a confidence score from the women’s health panel as a function of the time-series data and at least one of the first women’s health model and the second women’s health model. In another aspect, the plurality of women’s health models includes a women’s health classifier model, a women’s health prediction model, and a women’s health correlation model. In another aspect, training the women’s health model includes receiving a plurality of time-series data examples associated with the female classification, pretraining the women’s health model in the women’s health panel as a function of the plurality of time-series data examples by adjusting one or more parameter attributes of the women’s health model, and 11 Attorney Docket No.1518-126PCT1 training the women’s health model as a function of the one or more parameter attributes and a database. In another aspect, the first women’s health attribute includes a peripartum cardiomyopathy level, and the second women’s health attribute includes a coronary heart disease level. In another aspect, the women’s health attribute includes a women’s health attribute deviation, wherein the women’s health attribute deviation includes a change in the women’s health attribute. In another aspect, inputting the time-series data into the women’s health panel includes selecting the women’s health model from a plurality of women’s health models as a function of an input and a graphical user interface. In another aspect, displaying the women’s health attribute includes comparing the women’s health attribute to a nominal women’s health attribute, and displaying the women’s health attribute as a function of the comparison. In an aspect, an apparatus for generating a preoperative data structure using a pre- operative panel is described. The apparatus includes at least a processor, and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive subject data, wherein the subject data includes electrocardiogram (ECG) data, generate a plurality of panel outputs as a function of the subject data using a pre-operative panel machine-learning module, wherein the pre-operative panel machine-learning module includes a plurality of panel machine-learning models, wherein each of the plurality of panel machine-learning models is configured to generate one panel output for one panel focus as a function of the subject data, wherein generating the plurality of panel outputs includes generating a plurality of sets of panel training data, wherein the plurality of sets of panel training data includes correlations between exemplary subject data, exemplary panel focuses and exemplary panel outputs, training each of the plurality of panel machine-learning models using each of the plurality of sets of panel training data, and generating the plurality of panel outputs using the plurality of trained panel machine-learning models, and generate a pre-operative data structure as a function of the plurality of panel outputs. In another aspect, generating the plurality of panel outputs includes determining at least an ECG feature as a function of the ECG data, and determining the plurality of panel outputs as a 12 Attorney Docket No.1518-126PCT1 function of the ECG feature. In another aspect, determining the at least an ECG feature further includes generating ECG feature training data, wherein the ECG feature training data includes correlations between exemplary ECG data and exemplary ECG features, training an ECG feature machine-learning model using the ECG feature training data, and determining the at least an ECG feature using the trained ECG feature machine-learning model. In another aspect, the plurality of panel machine-learning models includes a first panel machine-learning model including a first panel focus related to coronary heart disease, wherein the first panel machine-learning model is configured to generate a first panel output related to the coronary heart disease as a function of the subject data. In another aspect, the plurality of panel machine-learning models includes a second panel machine-learning model including a second panel focus related to pulmonary hypertension, wherein the second panel machine-learning model is configured to generate a second panel output related to the pulmonary hypertension as a function of the subject data. In another aspect, the plurality of panel machine-learning models includes a third panel machine-learning model including a third panel focus related to atrial fibrillation, wherein the third panel machine-learning model is configured to generate a third panel output related to the atrial fibrillation as a function of the subject data. In another aspect, the plurality of panel machine-learning models includes a fourth panel machine-learning model including a fourth panel focus related to ejection fraction, wherein the fourth panel machine-learning model is configured to generate a fourth panel output related to the ejection fraction as a function of the subject data. In another aspect, the memory contains instructions further configuring the at least a processor to generate cohort training data, wherein the cohort training data includes correlations between exemplary subject data and exemplary subject cohorts, train a cohort classifier using the cohort training data, and classify the subject data to one or more subject cohorts using the trained cohort classifier. In another aspect, the memory contains instructions further configuring the at least a processor to update the panel training data as a function of an output of the cohort classifier. In another aspect, the plurality of panel outputs includes a pre-operative optimization output. 13 Attorney Docket No.1518-126PCT1 In an aspect, a method for generating a preoperative data structure using a pre-operative panel is described. The method includes receiving, using at least a processor, subject data, wherein the subject data includes electrocardiogram (ECG) data, generating, using the at least a processor, a plurality of panel outputs as a function of the subject data using a pre-operative panel machine-learning module, wherein the pre-operative panel machine-learning module includes a plurality of panel machine-learning models, wherein each of the plurality of panel machine-learning models is configured to generate one panel output for one panel focus as a function of the subject data, wherein generating the plurality of panel outputs includes generating a plurality of sets of panel training data, wherein the plurality of sets of panel training data includes correlations between exemplary subject data, exemplary panel focuses and exemplary panel outputs, training each of the plurality of panel machine-learning models using each of the plurality of sets of panel training data, and generating the plurality of panel outputs using the plurality of trained panel machine-learning models, and generating, using the at least a processor, a pre-operative data structure as a function of the plurality of panel outputs. In another aspect, generating the plurality of panel outputs includes determining, using the at least a processor, at least an ECG feature as a function of the ECG data, and determining, using the at least a processor, the plurality of panel outputs as a function of the ECG feature. In another aspect, determining the at least an ECG feature further includes generating, using the at least a processor, ECG feature training data, wherein the ECG feature training data includes correlations between exemplary ECG data and exemplary ECG features, training, using the at least a processor, an ECG feature machine-learning model using the ECG feature training data, and determining, using the at least a processor, the at least an ECG feature using the trained ECG feature machine-learning model. In another aspect, the plurality of panel machine-learning models includes a first panel machine-learning model including a first panel focus related to coronary heart disease, wherein the first panel machine-learning model is configured to generate a first panel output related to the coronary heart disease as a function of the subject data. In another aspect, the plurality of panel machine-learning models includes a second panel machine-learning model including a second panel focus related to pulmonary hypertension, wherein the second panel machine-learning model is configured to generate a second panel output related to the pulmonary hypertension as a function of the subject data. 14 Attorney Docket No.1518-126PCT1 In another aspect, the plurality of panel machine-learning models includes a third panel machine-learning model including a third panel focus related to atrial fibrillation, wherein the third panel machine-learning model is configured to generate a third panel output related to the atrial fibrillation as a function of the subject data. In another aspect, the plurality of panel machine-learning models includes a fourth panel machine-learning model including a fourth panel focus related to ejection fraction, wherein the fourth panel machine-learning model is configured to generate a fourth panel output related to the ejection fraction as a function of the subject data. In another aspect, the method further includes generating, using the at least a processor, cohort training data, wherein the cohort training data includes correlations between exemplary subject data and exemplary subject cohorts, training, using the at least a processor, a cohort classifier using the cohort training data, and classifying, using the at least a processor, the subject data to one or more subject cohorts using the trained cohort classifier. In another aspect, the method further includes updating, using the at least a processor, the panel training data as a function of an output of the cohort classifier. In another aspect, the plurality of panel outputs includes a pre-operative optimization output. The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the description below. Other features and advantages of the subject matter described herein will be apparent from the description and drawings, and from the claims. DESCRIPTION OF DRAWINGS For the purpose of illustrating the invention, the drawings show aspects of one or more embodiments of the invention. However, it should be understood that the present invention is not limited to the precise arrangements and instrumentalities shown in the drawings, wherein: FIG.1 is an exemplary embodiment of an apparatus for tracking cardiac indices; FIG.2 is an exemplary embodiment of an electrocardiogram (ECG); FIG.3 is a block diagram of an exemplary embodiment of a machine learning process; FIG.4 is a block diagram of an exemplary embodiment of a neural network; FIG.5 is a block diagram of an exemplary embodiment of a node of a neural network; FIG.6 is an illustration of an exemplary embodiment of fuzzy set comparison; 15 Attorney Docket No.1518-126PCT1 FIG.7 is an exemplary flow diagram illustrating a method for tracking cardiac indices; FIGS.8A and 8B are exemplary embodiments of a graphical user interface; FIG.9 is a block diagram of an apparatus for detecting hypertension attributes in a patient time- series data; FIG.10A is an exemplary embodiment of a system for instantiating a hypertension classifier model in accordance with the subject disclosure; FIG.10B is an exemplary embodiment of a system for instantiating a hypertension prediction model in accordance with the subject disclosure; FIG.10C is an exemplary embodiment of a system for instantiating a hypertension correlation model in accordance with the subject disclosure; FIG.11 is a block diagram of an exemplary method for detecting hypertension attributes in a patient time-series data; FIG.12 is a block diagram of an exemplary embodiment of an apparatus for identifying the progression of coronary heart disease; FIG.13 is a block diagram of an exemplary embodiment of a cardiac database; FIG.14 is an illustration of an exemplary embodiment of a chatbot; FIG.15 is an illustration of an exemplary cardiac report; FIG.16 is a flow diagram of an exemplary method for identifying the progression of coronary heart disease; FIG.17 is a block diagram of an apparatus for determining women’s health attributes in time series data; FIG.18A is an exemplary embodiment of a system for instantiating a women’s health classifier model in accordance with the subject disclosure; FIG.18B is an exemplary embodiment of a system for instantiating a women’s health prediction model in accordance with the subject disclosure; FIG.18C is an exemplary embodiment of a system for instantiating a women’s health correlation model in accordance with the subject disclosure; FIG.19 is a block diagram of an exemplary method for determining women’s health attributes in time series data; FIG.20 illustrates a block diagram of an exemplary apparatus for generating a preoperative data structure using a pre-operative panel; 16 Attorney Docket No.1518-126PCT1 FIG.21 illustrates an exemplary panel data structure displayed on a user device; FIG.22 illustrates a block diagram of an exemplary subject database; FIG.23 illustrates a block diagram of an exemplary pre-operative panel machine-learning module; FIG.24 illustrates a flow diagram of an exemplary method for generating a preoperative data structure using a pre-operative panel; and FIG.25 is a block diagram of a computing system that can be used to implement any one or more of the methodologies disclosed herein and any one or more portions thereof. The drawings are not necessarily to scale and may be illustrated by phantom lines, diagrammatic representations, and fragmentary views. In certain instances, details that are not necessary for an understanding of the embodiments or that render other details difficult to perceive may have been omitted. Like reference symbols in the various drawings indicate like elements. DETAILED DESCRIPTION Referring now to FIG.1, an apparatus 100 for tracking cardiac indices 104a-n is illustrated. Apparatus 100 comprises a processor 108. In one or more embodiments, processor 108 may include a computing device. Computing device could include any analog or digital control circuit, including an operational amplifier circuit, a combinational logic circuit, a sequential logic circuit, an application-specific integrated circuit (ASIC), a field programmable gate arrays (FPGA), or the like. Computing device may include a processor communicatively connected to a memory, as described above. Computing device may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor, and / or system on a chip as described in this disclosure. Computing device may include, be included in, and / or communicate with a mobile device such as a mobile telephone, smartphone, or tablet. Computing device may include a single computing device operating independently, or may include two or more computing device operating in concert, in parallel, sequentially, or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices. Computing device may interface or communicate with one or more additional devices as described below in further detail via a network interface device. Network interface device may be utilized for connecting computing device to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile 17 Attorney Docket No.1518-126PCT1 network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus, or other relatively small geographic space), a telephone network, a data network associated with a telephone / voice provider (e.g., a mobile communications provider data and / or voice network), a direct connection between two computing devices, and any combinations thereof. A network may employ a wired and / or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software etc.) may be communicated to and / or from a computer and / or a computing device. Computing device may include but is not limited to, for example, a first computing device or cluster of computing devices in a first location and a second computing device or cluster of computing devices in a second location. Computing device may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. Computing device may distribute one or more computing tasks as described below across a plurality of computing devices of computing device, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices. Computing device may be implemented, as a nonlimiting example, using a “shared nothing” architecture. With continued reference to FIG.1, computing device may be designed and / or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, computing device may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and / or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and / or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and / or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Computing device may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and / or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and / or processes may be performed according to any protocol suitable for division of tasks between iterations. A person of ordinary skill in the art, upon reviewing the entirety of this 18 Attorney Docket No.1518-126PCT1 disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and / or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and / or parallel processing. More details regarding computing devices will be described below. With continued reference to FIG.1, apparatus 100 includes a memory 112 communicatively connected to processor 108, wherein the memory 112 contains instructions configuring the processor 108 to perform any processing steps described herein. For the purposes of this disclosure, “communicatively connected” means connected by way of a connection, attachment, or linkage between two or more relata which allows for reception and / or transmittance of information therebetween. For example, and without limitation, this connection may be wired or wireless, direct, or indirect, and between two or more components, circuits, devices, systems, and the like, which allows for reception and / or transmittance of data and / or signal(s) therebetween. Data and / or signals therebetween may include, without limitation, electrical, electromagnetic, magnetic, video, audio, radio, and microwave data and / or signals, combinations thereof, and the like, among others. A communicative connection may be achieved, for example and without limitation, through wired or wireless electronic, digital, or analog, communication, either directly or by way of one or more intervening devices or components. Further, communicative connection may include electrically coupling or connecting at least an output of one device, component, or circuit to at least an input of another device, component, or circuit. For example, and without limitation, using a bus or other facility for intercommunication between elements of a computing device. Communicative connecting may also include indirect connections via, for example and without limitation, wireless connection, radio communication, low-power wide-area network, optical communication, magnetic, capacitive, or optical coupling, and the like. In some instances, the terminology “communicatively coupled” may be used in place of communicatively connected in this disclosure. With continued reference to FIG.1, computing device may perform determinations, classification, and / or analysis steps, methods, processes, or the like as described in this disclosure using machine learning processes. For the purposes of this disclosure, a “machine learning process” is a process that automatedly uses a body of data known as “training data” and / or a “training set” to generate an algorithm that will be performed by a processor module to produce outputs given data provided as inputs; this is in contrast to a nonmachine learning 19 Attorney Docket No.1518-126PCT1 software program where the commands to be executed are determined in advance by a user and written in a programming language. A machine learning process may utilize supervised, unsupervised, lazy-learning processes and / or neural networks. More details regarding computing devices and machine learning processes will be provided below. With continued reference to FIG.1, in one or more embodiments, one or more machine learning models may be used to perform certain function or functions of apparatus 100, such as generation of one or more cardiac indices 104a-n from cardiac panel 116, as described below. Processor 108 may use a machine learning module to implement one or more algorithms as described herein or generate one or more machine learning models, such as cardiac machine learning model, as described below. However, machine learning module is exemplary and may not be necessary to generate one or more machine learning models and perform any machine learning described herein. In one or more embodiments, one or more machine learning models may be generated using training data. Training data may include inputs and corresponding predetermined outputs so that machine learning model may use correlations between the provided exemplary inputs and outputs to develop an algorithm and / or relationship that then allows the machine learning model to determine its own outputs for inputs. Training data may contain correlations that a machine learning process may use to model relationships between two or more categories of data elements. Exemplary inputs and outputs may be retrieved from a database, selected from one or more electronic health records (EHRs), or be provided by a user such as a software developer or medical professional. In one or more embodiments, machine learning module may obtain training data by querying a communicatively connected database that includes past inputs and outputs. Training data may include inputs from various types of databases, resources, and / or user inputs and outputs correlated to each of those inputs, so that machine learning model may determine an output. Correlations may indicate causative and / or predictive links between data, which may be modeled as relationships, such as mathematical relationships, by machine learning models, as described in further detail below. In one or more embodiments, training data may be formatted and / or organized by categories of data elements by, for example, associating data elements with one or more descriptors corresponding to categories of data elements. As a nonlimiting example, training data may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data 20 Attorney Docket No.1518-126PCT1 may be linked to descriptors of categories by tags, tokens, or other data elements. In one or more embodiments, training data may include previous outputs such that one or more machine learning models may iteratively produce outputs. With continued reference to FIG.1, in one or more embodiments, processor 108 may implement one or more aspects of “generative artificial intelligence (AI)”, a type of AI that uses machine learning algorithms to create, establish, or otherwise generate data such as, without limitation, cardiac indices or other interpretations of medical data. In one or more embodiments, machine learning module described below in this disclosure may generate one or more generative machine learning models that are trained on one or more prior iterations. One or more generative machine learning models may be configured to generate new examples that are similar to the training data of the one or more generative machine learning models but are not exact replicas; for instance, and without limitation, data quality or attributes of the generated examples may bear a resemblance to the training data provided to one or more generative machine learning models, wherein the resemblance may pertain to underlying patterns, features, or structures found within the provided training data. With continued reference to FIG.1, processor 108 is configured to receive cardiac input data 120 from a patient comprising a plurality of cardiac signals. For the purposes of this disclosure, “cardiac input data” are input data describing one or more biological, anatomical, physiological, or biomedical features or functions of the heart of patient. For the purposes of this disclosure, “input data” are data that may be used as an input or a query to match other data or information and / or to selectively retrieve data or information for use in further method steps as disclosed below. In one or more embodiments, cardiac input data 120 may include medical data collected by a medical professional and / or results generated therefrom, such as without limitation pathology test results, X-ray data, echocardiogram (ECG), magnetic resonance imaging (MRI) data, computed tomography (CT) data, ultrasound imaging data including intracardiac echocardiogram (ICE), transthoracic echocardiogram frame, and / or transesophageal echocardiogram (TEE) data, optical images, digital photographs, and / or the like. For the purposes of this disclosure, an “electrocardiogram (ECG)” is a recording of electrical activity of patient’s heart over a period of time; “ECG” and “ECG data” may be used interchangeably throughout this disclosure. In one or more embodiments, ECG data may include one or more recordings captured by a plurality (e.g., 12) of electrodes placed on patient’s skin. In one or more 21 Attorney Docket No.1518-126PCT1 embodiments, ECG data may include information regarding a P wave, T wave, QRS complex, PR interval, ST segment, and / or the like, as described in detail below in this disclosure. In one or more embodiments, ECG data may be used to identify specific cardiac events or phases of a cardiac cycle, e.g., isovolumic relaxation, ventricular filling, isovolumic contraction, and rapid ventricular ejection. For the purposes of this disclosure, computed tomography (CT) is a medical imaging technique that uses X-rays to capture cross-sectional images (slices) of a patient’s body; by taking a plurality of slices, a CT scan creates a detailed three-dimensional (3D) representation of internal structures. For the purposes of this disclosure, an “ICE frame” is a 2D ultrasound image that represents anatomy (i.e., walls, chambers, blood vessels, etc.) of at least part of a heart, as described above. For the purposes of this disclosure, a “transthoracic echocardiogram (TTE) frame” is a two-dimensional (2D) ultrasound image collected by placing a probe or ultrasound transducer on patient’s chest or abdomen to collect various views of heart. For the purposes of this disclosure, a “transesophageal echocardiogram (TEE) frame” is a 2D ultrasound image collected by passing a specialized probe containing an ultrasound transducer at its tip into patient’s esophagus; it is an alternative way of performing echocardiography. For the purposes of this disclosure, “echocardiography” is an imaging technique that uses ultrasound to examine a heart, the resulting visual image of which is an ECG. With continued reference to FIG.1, for the purposes of this disclosure, a “cardiac signal” is a signal within, or a subset of, cardiac input data 120 collected from part of a heart or otherwise describes the anatomy or function of part of a heart. For the purposes of this disclosure, a “signal” is any intelligible representation of data, for example from one device to another. A signal may include an optical signal, a hydraulic signal, a pneumatic signal, a mechanical signal, an electric signal, a digital signal, an analog signal, and the like. In some cases, a signal may be used to communicate with a computing device, for example by way of one or more ports. In some cases, a signal may be transmitted and / or received by computing device for example by way of an input / output port. An analog signal may be digitized, for example by way of an analog to digital converter. In some cases, an analog signal may be processed, for example by way of any analog signal processing steps described in this disclosure, prior to digitization. In some cases, a digital signal may be used to communicate between two or more devices, including without limitation computing devices. In some cases, a digital signal may be communicated by way of one or more communication protocols, including without limitation 22 Attorney Docket No.1518-126PCT1 internet protocol (IP), controller area network (CAN) protocols, serial communication protocols (e.g., universal asynchronous receiver-transmitter [UART]), parallel communication protocols (e.g., IEEE 1284 [printer port]), and the like. With continued reference to FIG.1, for the purposes of this disclosure, a “patient” is a human or any individual organism, on whom or on which a procedure, study, or otherwise experiment, may be conducted. As nonlimiting examples, patient may include human patient with symptoms of systolic or diastolic dysfunction, an individual undergoing cardiac screening, a participant in a clinical trial, an individual with congenital heart disease, a heart transplant candidate, an individual receiving follow-up care after cardiac surgery, a healthy volunteer, an individual with heart failure, or the like. Additionally or alternatively, patient may include an animal model (i.e., an animal used to model certain medical conditions such as a laboratory rat). With continued reference to FIG.1, in one or more embodiments, cardiac input data 120 may be associated with one or more EHRs of patient. For the purposes of this disclosure, an electronic health record (EHR) is a comprehensive collection of records relating to the health history, diagnosis, or condition of patient, relating to treatment provided or proposed to be provided to the patient, or relating to additional factors that may impact the health of the patient; elements within an EHR, once combined, may provide a detailed picture of patient's overall health. In one or more embodiments, cardiac input data 120 may be deposited to and retrieved from one or more EHRs. In one or more embodiments, EHR may include demographic data of patient; for example, and without limitation, EHR may include basic information about patient such as name, age, gender, ethnicity, socioeconomic status, and / or the like. In one or more embodiments, each EHR may also include patient’s medical history; for example, and without limitation, EHR may include a detailed record of patient's past health conditions, medical procedures, hospitalizations, and illnesses such as surgeries, treatments, medications, allergies, and / or the like. In one or more embodiments, each EHR may include lifestyle information of patient; for example, and without limitation, EHR may include details about the patient's diet, exercise habits, drug use, smoking and alcohol consumption, and other behaviors that could impact patient’s health. In one or more embodiments, EHR may include patient’s family history; for example, and without limitation, EHR may include a record of hereditary diseases. In one or more embodiments, a database may comprise a plurality of EHRs. In one or more embodiments, EHRs may be retrieved from a repository of similar nature as database. 23 Attorney Docket No.1518-126PCT1 With continued reference to FIG.1, apparatus 100 may include or be coupled to a database 124. Database 124 may include any type of database and / or may be implemented in any manner suitable for implementation of databases. Database 124 may be implemented, without limitation, as a relational database, a key-value retrieval database such as a NoSQL database, or any other format or structure for use as database that a person of ordinary skill in the art would recognize as suitable upon review of the entirety of this disclosure. Database 124 may alternatively or additionally be implemented using a distributed data storage protocol and / or data structure, such as a distributed hash table or the like. Database 124 may include a plurality of data entries and / or records as described in this disclosure. Data entries in database 124 may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and / or in linked tables such as tables related by one or more indices in database 124 or another relational database. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in database 124 may store, retrieve, organize, and / or reflect data and / or records as used herein, as well as categories and / or populations of data consistently with this disclosure. With continued reference to FIG.1, in one or more embodiments, cardiac input data 120 may include at least an image, such as an image of an ECG or an image of a CT scan. In such cases, processor 108 and / or computing device may transform one or more images within cardiac input data 120 to one or more high-quality images and build subsequent downstream tasks using the one or more high-quality images. In one or more embodiments, processor 108 may be configured to transform images into in-silicon images by extracting a plurality of cardiac parameters from the images, converting the plurality of cardiac parameters to one or more digitized signals, and transform the one or more digitized signals into the in-silicon images. For the purposes of this disclosure, an “in-silicon image” is a computer-generated, abstract representation of a real image after eliminating noises, defects, aberrations, backgrounds, and the like. In some cases, images containing time-dependent cardiac input data 120 may be converted and simplified to time series data (i.e., ^^^^^^ as a function of ^^), as described below. In some cases, transforming images into in-silicon images may include transforming the images into in- silicon images using a transformer model. Downstream models may be trained using these transformed images, which eliminates the need for having images of different qualities in the dataset for different downstream tasks. 24 Attorney Docket No.1518-126PCT1 With continued reference to FIG.1, additionally and / or alternatively, in one or more embodiments, receiving cardiac input data 120 may comprise comparing the cardiac input data 120 against at least a quality assurance parameter. In one or more embodiments, receiving cardiac input data 120 may comprise validating one or more digitized signals by classifying the one or more digitized signals to a plurality of preliminary parameters and determining an accuracy status of plurality of cardiac parameters, as described above, by comparing the plurality of preliminary parameters to the plurality of cardiac parameters, and generating a quality diagnostic of the cardiac input data based on the result of the validation. With continued reference to FIG.1, in one or more embodiments, cardiac input data 120 may contain digital files, wherein processor 108 may perform one or more functions of apparatus 100 by using optical character recognition (OCR) to read the digital files and extract information therein. In one or more embodiments, OCR may include automatic conversion of images (e.g., typed, handwritten, or printed text) into machine-encoded text. In one or more embodiments, recognition of at least a keyword from an image component may include one or more processes, including without limitation OCR, optical word recognition, intelligent character recognition, intelligent word recognition, and the like. In one or more embodiments, OCR may recognize written text one glyph or character at a time, for example, for languages that use a space as a word divider. In one or more embodiments, intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine learning processes. In one or more embodiments, intelligent word recognition (IWR) may recognize written text, one word at a time, for instance by employing machine learning processes. With continued reference to FIG.1, in one or more embodiments, OCR may employ preprocessing of image components. Preprocessing process may include without limitation de- skew, de-speckle, binarization, line removal, layout analysis or “zoning,” line and word detection, script recognition, character isolation or “segmentation,” and normalization. In one or more embodiments, a de-skew process may include applying a transform (e.g., homography or affine transform) to an image component to align text. In one or more embodiments, a de-speckle process may include removing positive and negative spots and / or smoothing edges. In one or more embodiments, a binarization process may include converting an image from color or greyscale to black-and-white (i.e., a binary image). Binarization may be performed as a simple 25 Attorney Docket No.1518-126PCT1 way of separating text (or any other desired image component) from the background of image component. In one or more embodiments, binarization may be required for example if an employed OCR algorithm only works on binary images. In one or more embodiments, line removal process may include removal of non-glyph or non-character imagery (e.g., boxes and lines). In one or more embodiments, a layout analysis or “zoning” process may identify columns, paragraphs, captions, and the like as distinct blocks. In one or more embodiments, a line and word detection process may establish a baseline for word and character shapes and separate words, if necessary. In one or more embodiments, a script recognition process may, for example in multilingual documents, identify a script, allowing an appropriate OCR algorithm to be selected. In one or more embodiments, a character isolation or “segmentation” process may separate signal characters, for example, character-based OCR algorithms. In one or more embodiments, a normalization process may normalize the aspect ratio and / or scale of image component. With continued reference to FIG.1, in one or more embodiments, an OCR process may include an OCR algorithm. Exemplary OCR algorithms include matrix-matching processes and / or feature extraction processes. Matrix matching may involve comparing an image to a stored glyph on a pixel-by-pixel basis. In one or more embodiments, matrix matching may also be known as “pattern matching,” “pattern recognition,” and / or “image correlation.” Matrix matching may rely on an input glyph being correctly isolated from the rest of image component. Matrix matching may also rely on a stored glyph being in a similar font and at the same scale as input glyph. With continued reference to FIG.1, in one or more embodiments, an OCR process may include a feature extraction process. In one or more embodiments, feature extraction may decompose a glyph into features. Exemplary nonlimiting features may include corners, edges, lines, closed loops, line direction, line intersections, and the like. In one or more embodiments, feature extraction may reduce the dimensionality of representation and may make the recognition process computationally more efficient. In one or more embodiments, extracted features can be compared with an abstract vector-like representation of a character, which might be reduced to one or more glyph prototypes. General techniques of feature detection in computer vision are applicable to this type of OCR. In one or more embodiments, machine learning process like nearest neighbor classifiers (e.g., k-nearest neighbors algorithm) can be used to compare image 26 Attorney Docket No.1518-126PCT1 features with stored glyph features and choose a nearest match. OCR may employ any machine learning process described in this disclosure. Exemplary nonlimiting OCR software includes Cuneiform and Tesseract. Cuneiform is a multi-language, open-source OCR system originally developed by Cognitive Technologies of Moscow, Russia. Tesseract is a free OCR software originally developed by Hewlett-Packard of Palo Alto, California, United States. With continued reference to FIG.1, in one or more embodiments, OCR may employ a two-pass approach to character recognition. Second pass may include adaptive recognition and use letter shapes recognized with high confidence on a first pass to better recognize remaining letters on a second pass. In one or more embodiments, two-pass approach may be advantageous for unusual fonts or low-quality image components where visual verbal content may be distorted. Another exemplary OCR software tool includes OCRopus. The development of OCRopus is led by the German Research Center for Artificial Intelligence in Kaiserslautern, Germany. In one or more embodiments, OCR software may employ neural networks, for example, deep neural networks, as described in this disclosure below. With continued reference to FIG.1, in one or more embodiments, OCR may include post-processing. For example, OCR accuracy can be increased, in some cases, if output is constrained by a lexicon. A lexicon may include a list or set of words that are allowed to occur in a document. In one or more embodiments, a lexicon may include, for instance, all the words in the English language, or a more technical lexicon for a specific field. In some cases, an output stream may be a plain text stream or file of characters. In one or more embodiments, an OCR may preserve an original layout of visual verbal content. In one or more embodiments, near- neighbor analysis can make use of co-occurrence frequencies to correct errors by noting that certain words are often seen together. For example, “Washington, D.C.” is generally far more common in English than “Washington DOC.” In one or more embodiments, an OCR process may make use of a priori knowledge of grammar for a language being recognized. For example, OCR process may apply grammatical rules to help determine if a word is likely to be a verb or a noun. Distance conceptualization may be employed for recognition and classification. For example, a Levenshtein distance algorithm may be used in OCR post-processing to further optimize results. A person of ordinary skill in the art will recognize how to apply the aforementioned technologies to extract information from a digital file upon reviewing the entirety of this disclosure. 27 Attorney Docket No.1518-126PCT1 With continued reference to FIG.1, in one or more embodiments, a computer vision module configured to perform one or more computer vision tasks such as, without limitation, object recognition, feature detection, edge / corner detection thresholding, or machine learning process may be used to recognize specific features or attributes. For the purposes of this disclosure, a “computer vision module” is a computational component designed to perform one or more computer vision, image processing, and / or modeling tasks. In one or more embodiments, computer vision module may receive one or more digital files containing one or more features from a data repository and generate one or more labels as a function of the received one or more features. In one or more embodiments, to generate plurality of labels, computer vision module may be configured to compare one or more features against the statistical data of the one or more features and attach one or more labels as a function of the comparison. With continued reference to FIG.1, in one or more embodiments, computer vision module may include an image processing module, wherein images may be pre-processed using the image processing module. For the purposes of this disclosure, an “image processing module” is a component designed to process digital images such as images described herein. For example, and without limitation, image processing module may be configured to compile a plurality of images of a multi-layer scan to create an integrated image. In one or more embodiments, image processing module may include a plurality of software algorithms that can analyze, manipulate, or otherwise enhance an image, such as, without limitation, a plurality of image processing techniques as described below. In one or more embodiments, computer vision module may also include hardware components such as, without limitation, one or more graphics processing units (GPUs) that can accelerate the processing of a large number of images. In one or more embodiments, computer vision module may be implemented with one or more image processing libraries such as, without limitation, OpenCV, PIL / Pillow, ImageMagick, and the like. In a nonlimiting example, in order to recognize one or more features within images from cardiac input data 120, one or more image processing tasks, such as noise reduction, contrast enhancement, intensity normalization, image segmentation, and / or the like, may be performed by computer vision module on the images to isolate certain features or components from the rest. In one or more embodiments, one or more machine learning models may be used to perform segmentations, for example, and without limitation, a U-net (i.e., a convolution neural network containing a contracting path as an encoder and an expansive path as a decoder, wherein the 28 Attorney Docket No.1518-126PCT1 encoder and the decoder forms a U-shaped structure). A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various image processing, computer vision, and modeling tasks that may be performed by processor 108. With continued reference to FIG.1, in one or more embodiments, one or more functions of apparatus 100 may involve a use of image classifiers to classify images within any data described in this disclosure. For the purposes of this disclosure, an “image classifier” is a machine learning model, such as a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” as described in further detail below, that sort inputs of image information into categories or bins of data, outputting the categories or bins of data and / or labels associated therewith. Image classifier may be configured to output at least a datum that labels or otherwise identifies a set of images that are clustered together, found to be close under a distance metric as described below, or the like. Computing device and / or another device may generate image classifier using a classification algorithm. For the purposes of this disclosure, a classification algorithm is a process whereby computing device derives a classifier from training data. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and / or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, Fisher’s linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and / or neural network-based classifiers. In one or more embodiments, processor 108 may use image classifier to identify a key image in any data described in this disclosure. For the purposes of this disclosure, a “key image” is an element of visual data used to identify and / or match elements to each other. In one or more embodiments, key image may include part of a medical image, such an image of an ECG, with features that unambiguously identify the type of the medical image. Image classifier may be trained with binarized visual data that have already been classified to determine key images in any other data described in this disclosure. For the purposes of this disclosure, “binarized visual data” are visual data that are described in a binary format. For example, binarized visual data of a photo may comprise ones and zeroes, wherein the specific sequence of ones and zeros may be used to represent the photo. Binarized visual data may be used for image recognition wherein a specific sequence of ones and zeroes may indicate a product present in the image. An image classifier may be consistent with any classifier as 29 Attorney Docket No.1518-126PCT1 discussed herein. An image classifier may receive input data described in this disclosure and output a key image with the data. In one or more embodiments, image classifier may be used to compare visual data in one data set with visual data in another data set. With continued reference to FIG.1, processor 108 may be configured to perform feature extraction on one or more images within cardiac input data 120. For the purposes of this disclosure, “feature extraction” is a process of transforming an initial data set into informative measures and values. For example, feature extraction may include a process of determining one or more geometric features of an anatomic structure. In one or more embodiments, feature extraction may be used to determine one or more spatial relationships within a drawing that may be used to uniquely identify one or more features. In one or more embodiments, processor 108 may be configured to extract one or more regions of interest, wherein the regions of interest may be used to extract one or more features using one or more feature extraction techniques. With continued reference to FIG.1, processor 108 may be configured to perform one or more of its functions, such as generation of one or more cardiac indices, as described below, using a feature learning algorithm. For the purposes of this disclosure, a “feature learning algorithm” is a machine learning algorithm that identifies associations between elements of data in a data set, which may include without limitation a training data set, where particular outputs and / or inputs are not specified. For instance, and without limitation, a feature learning algorithm may detect co-occurrences of elements of data, as defined above, with each other. Computing device may perform feature learning algorithm by dividing elements or sets of data into various sub-combinations of such data to create new elements of data and evaluate which elements of data tend to co-occur with which other elements. In one or more embodiments, feature learning algorithm may perform clustering of data. With continued reference to FIG.1, feature learning and / or clustering algorithm may be implemented, as a nonlimiting example, using a k-means clustering algorithm. For the purposes of this disclosure, a “k-means clustering algorithm” is a type of cluster analysis that partitions n observations or unclassified cluster data entries into k clusters in which each observation or unclassified cluster data entry belongs to the cluster with the nearest mean. For the purposes of this disclosure, “cluster analysis” is a process that includes grouping a set of observations or data entries in way that observations or data entries in the same group or cluster are more similar to each other than to those in other groups or clusters. Cluster analysis may be performed by 30 Attorney Docket No.1518-126PCT1 various cluster models that include connectivity models such as hierarchical clustering, centroid models such as k-means, distribution models such as multivariate normal distribution, density models such as density-based spatial clustering of applications with nose (DBSCAN) and ordering points to identify the clustering structure (OPTICS), subspace models such as biclustering, group models, graph-based models such as a clique, signed graph models, neural models, and the like. Cluster analysis may include hard clustering, whereby each observation or unclassified cluster data entry belongs to a cluster or not. Cluster analysis may include soft clustering or fuzzy clustering, as described below, whereby each observation or unclassified cluster data entry belongs to each cluster to a certain degree such as for example a likelihood of belonging to a cluster; for instance, and without limitation, a fuzzy clustering algorithm may be used to identify clustering of elements of a first type or category with elements of a second type or category, and vice versa, as described below. Cluster analysis may include strict partitioning clustering, whereby each observation or unclassified cluster data entry belongs to exactly one cluster. Cluster analysis may include strict partitioning clustering with outliers, whereby observations or unclassified cluster data entries may belong to no cluster and may be considered outliers. Cluster analysis may include overlapping clustering whereby observations or unclassified cluster data entries may belong to more than one cluster. Cluster analysis may include hierarchical clustering, whereby observations or unclassified cluster data entries that belong to a child cluster also belong to a parent cluster. With continued reference to FIG.1, computing device may generate a k-means clustering algorithm by receiving unclassified data and outputting a definite number of classified data entry clusters, wherein the data entry clusters each contain cluster data entries. K-means algorithm may select a specific number of groups or clusters to output, identified by a variable “k.” Generating k-means clustering algorithm includes assigning inputs containing unclassified data to a “k-group” or “k-cluster” based on feature similarity. Centroids of k-groups or k-clusters may be utilized to generate classified data entry cluster. K-means clustering algorithm may select and / or be provided “k” variable by calculating k-means clustering algorithm for a range of k values and comparing results. K-means clustering algorithm may compare results across different values of k as the mean distance between cluster data entries and cluster centroid. K- means clustering algorithm may calculate mean distance to a centroid as a function of k value, and the location of where the rate of decrease starts to sharply shift, which may be utilized to 31 Attorney Docket No.1518-126PCT1 select a k value. Centroids of k-groups or k-cluster include a collection of feature values which are utilized to classify data entry clusters containing cluster data entries. K-means clustering algorithm may act to identify clusters of closely related data, which may be provided with user cohort labels; this may, for instance, generate an initial set of user cohort labels from an initial set of data, and may also, upon subsequent iterations, identify new clusters to be provided new labels, to which additional data may be classified, or to which previously used data may be reclassified. With continued reference to FIG.1, generating a k-means clustering algorithm may include generating initial estimates for k centroids which may be randomly generated or randomly selected from unclassified data input. K centroids may be utilized to define one or more clusters. K-means clustering algorithm may assign unclassified data to one or more k- centroids based on the squared Euclidean distance by first performing a data assigned step of unclassified data. K-means clustering algorithm may assign unclassified data to its nearest centroid based on the collection of centroids ^^^^ of centroids in set ^^. Unclassified data may beassigned to a cluster based on ^^^^^^^^^^^^ ଶ^^ ∋ ^ ^^^^^^^^^^^^^, ^^^ , where ^^^^^^^^^^^^ includes argument ofthe minimum, ^^^^ includes a collection of centroids in a set ^^, and ^^^^^^^^ includes standard Euclidean distance. K-means clustering module may then recompute centroids by taking a meanof all cluster data entries assigned to a centroid’s cluster. This may be calculated based on ^^^^ ൌ1 / |^^^^|∑ ^^^^ ∋ ^^^^ ௫^. K-means clustering algorithm may continue to repeat these calculations untila stopping criterion has been satisfied such as when cluster data entries do not change clusters, the sum of the distances have been minimized, and / or some maximum number of iterations has been reached. With continued reference to FIG.1, k-means clustering algorithm may be configured to calculate a degree of similarity index value. For the purposes of this disclosure, a “degree of similarity index value” is a distance measured between each data entry cluster generated by k- means clustering algorithm and a selected element. Degree of similarity index value may indicate how close a particular combination of elements is to being classified by k-means algorithm to a particular cluster. K-means clustering algorithm may evaluate the distances of the combination of elements to the k-number of clusters output by k-means clustering algorithm. Short distances between an element of data and a cluster may indicate a higher degree of similarity between the element of data and a particular cluster. Longer distances between an element and a cluster may 32 Attorney Docket No.1518-126PCT1 indicate a lower degree of similarity between the element to be compared and / or clustered and a particular cluster. With continued reference to FIG.1, k-means clustering algorithm selects a classified data entry cluster as a function of the degree of similarity index value. In one or more embodiments, k-means clustering algorithm may select a classified data entry cluster with the smallest degree of similarity index value indicating a high degree of similarity between an element and the data entry cluster. Alternatively or additionally, k-means clustering algorithm may select a plurality of clusters having low degree of similarity index values to elements to be compared and / or clustered thereto, indicative of greater degrees of similarity. Degree of similarity index values may be compared to a threshold number indicating a minimal degree of relatedness suitable for inclusion of a set of element data in a cluster, where degree of similarity indices a-n falling under the threshold number may be included as indicative of high degrees of relatedness. The above- described illustration of feature learning using k-means clustering is included for illustrative purposes only and should not be construed as limiting potential implementation of feature learning algorithms; a person of ordinary skills in the art, upon reviewing the entirety of this disclosure, will be aware of various additional or alternative feature learning approaches, such as particle swarm optimization (PSO) and generative adversarial network (GAN) that may be used consistently with this disclosure. With continued reference to FIG.1, in one or more embodiments, processor 108 may use an image recognition algorithm to determine patterns within an image. In one or more embodiments, image recognition algorithm may include an edge-detection algorithm, which may detect one or more shapes defined by edges. For the purposes of this disclosure, an “edge detection algorithm” is or includes a mathematical method that identifies points in a digital image at which the image brightness changes sharply and / or has discontinuities. In one or more embodiments, such points may be organized into straight and / or curved line segments, which may be referred to as “edges.” Edge detection may be performed using any suitable edge detection algorithm, including without limitation Canny edge detection, Sobel operator edge detection, Prewitt operator edge detection, Laplacian operator edge detection, and / or differential edge detection. Edge detection may include phase congruency-based edge detection, which finds all locations of an image where all sinusoids in the frequency domain, for instance when generated using a Fourier decomposition, may have matching phases which may indicate a 33 Attorney Docket No.1518-126PCT1 location of an edge. With continued reference to FIG.1, processor 108 is configured to input cardiac input data 120 into a cardiac panel 116, the cardiac panel 116 comprising a plurality of cardiac models 128a-n. For the purposes of this disclosure, a “cardiac panel” is a set of algorithms, machine learning models, or mathematical models that are used to collectively evaluate and monitor one or more heart-related medical conditions. As a nonlimiting example, cardiac panel 116 may include a set of algorithms configured to receive and analyze one or more ECGs associated with patient. As another nonlimiting example, cardiac panel 116 may include a set of algorithms configured to use one or more ECGs as input and output information associated with one or more heart conditions such as hyperkalemia, systolic dysfunction, diastolic dysfunction, pulmonary hypertension, and / or the like. For the purposes of this disclosure, a “cardiac model” is an algorithm, a machine learning model, or a mathematical model including a regression model configured to receive an input variable such as an ECG signal from an ECG and output one or more suggested medical conditions and / or similar information that may be indicative of one or more heart-related medical conditions. In one or more embodiments, cardiac models 128a-n may contain a specific algorithm configured to monitor a particular indicator associated with a heart condition, such as a decreased left ventricular ejection fraction (LVEF) indicative of a systolic dysfunction or an elevated left ventricular filling pressure (LVFP) indicative of a diastolic dysfunction, as described below. In one or more embodiments, plurality of cardiac models 128a- n may be configured to evaluate a plurality of indicators from complementary angles to increase the overall accuracy of detection for cardiac panel 116. In one or more embodiments, cardiac models 128a-n may be directed toward a particular heart condition such as without limitation, hyperkalemia, systolic dysfunction, diastolic dysfunction, pulmonary hypertension, arrythmias, tachycardia, bradycardia, heart attacks, coronary heart disease, and the like. In one or more embodiments, cardiac panel 116 and / or one or more cardiac models 128a-n contained therein may be configured to provide a comprehensive view of a patient's journey through a heart condition using time series data, as described below. With continued reference to FIG.1, at least one cardiac models 128a-n of cardiac panel 116 is configured to calculate a cardiac index 104 associated with a heart condition. For the purposes of this disclosure, a “cardiac index” is a metric that describes and / or evaluates one or more aspects related to the overall health of patient’s heart. In some cases, cardiac index 104 34 Attorney Docket No.1518-126PCT1 may include a binary metric such as “yes” vs “no”, or “likely” vs. “unlikely”. In some other cases, cardiac index 104 may be selected from a preset scale such as 85 / 100 or 6 / 10. In some other cases, cardiac index 104 may include a descriptor selected from a group of preset descriptors such as one of “very good”, “good”, “poor”, and “very poor”. In one or more embodiments, cardiac index 104 may include a probability associated a heart condition, such as 80%, wherein the probability indicates the likelihood that the patient has developed the heart condition and / or will develop the heart condition in the future. With continued reference to FIG.1, as a nonlimiting example, at least one cardiac index 104 of one or more cardiac indices 104a-n may include an ejection fraction (EF) level, such as an EF of 50%. For the purposes of this disclosure, an “ejection fraction (EF)” or “ejection fraction (EF) level” is the volumetric fraction of blood ejected from a chamber of heart, such as the left ventricle, with each heart contraction or heartbeat; it is widely used as a measure of the pumping efficiency of the heart. The EF of the left heart, known as the left ventricular ejection fraction (LVEF), is calculated by dividing the volume of blood pumped from the left ventricle per heartbeat (i.e., stroke volume) by the volume of blood present in the left ventricle at the end of diastolic filling (i.e., end-diastolic volume); LVEF is an indicator of the effectiveness of pumping blood into the systemic circulation, and a reduced LVEF level may indicate a case of systolic dysfunction. In contrast, the EF of the right heart, known as the right ventricular ejection fraction (RVEF), is a measure of the efficiency of pumping blood into the pulmonary circulation. As another nonlimiting example, at least one cardiac index 104 of one or more cardiac indices 104a- n may include an elevated LVFP, e.g., an LVFP of 18 mmHg. For the purposes of this disclosure, “left ventricular filling pressure (LVFP)” is the pressure that fills the left ventricle in diastole and determines stroke volume; an elevated LVFP may be indicative of a case of diastolic dysfunction. As another nonlimiting example, at least one cardiac index 104 of one or more cardiac indices 104a-n may be associated with pulmonary hypertension, e.g., a pulmonary artery pressure of 80 mmHg. For the purposes of this disclosure, “pulmonary hypertension” is a type of high blood pressure that affects pulmonary arteries and the right side of the heart; it is commonly seen among patients with left ventricular diastolic dysfunction. Alternatively and / or additionally, cardiac index 104 may be directed to other heart conditions such as atrial fibrillation, hyperkalemia, arrythmias, tachycardia, bradycardia, premature beats, bradycardia, heart block, heart palpitations, heart attacks, coronary heart disease, and / or the like. 35 Attorney Docket No.1518-126PCT1 With continued reference to FIG.1, at least one cardiac models 128a-n comprises at least one trained cardiac machine learning model 132 configured to receive cardiac input data 120 as inputs and output cardiac indices 104a-n. In one or more embodiments, training at least one cardiac machine learning model 132 may comprise receiving a plurality of cardiac training data 136 associated with a plurality of patients, pretraining the at least one cardiac machine learning model 132 as a function of the plurality of cardiac training data 136 by adjusting one or more parameters within the at least one cardiac machine learning model 132, and retraining the at least one cardiac machine learning model 132 as a function of the adjusted one or more parameters and a labeled subset of cardiac training data 136. As a nonlimiting example, cardiac machine learning model 132 may be pretrained using a generic set of cardiac training data 136 containing a large quantity of time series data (see below), and retrained using a subset of the cardiac training data 136 that are exclusively ECGs. Implementation of cardiac machine learning model 132 may be consistent with any type of machine learning model or algorithm described in this disclosure. In one or more embodiments, training cardiac machine learning model 132 may include training cardiac machine learning model using a transformer architecture. In one or more embodiments, cardiac training data 136 may include data specifically synthesized for training purposes using one or more generative models, as described in this disclosure. As a nonlimiting example, cardiac training data 136 may be extracted from medical literature using a large language model (LLM). In one or more embodiments, one or more historic queries may be incorporated into cardiac training data 136 upon validation. In one or more embodiments, cardiac training data 136 may be retrieved from one or more databases, EHRs, and / or other repositories of similar nature, or be supplied as one or more user inputs. In one or more embodiments, at least a portion of cardiac training data 136 may be added, deleted, replaced, or otherwise updated as a function of one or more inputs from one or more users. Additional details are described below in this disclosure. With continued reference to FIG.1, at least one cardiac models 128a-n is configured to calculate a cardiac index 104 associated with diastolic dysfunction. For the purposes of this disclosure, “diastolic dysfunction” is a dysfunction in diastole where a chamber of heart does not relax as it should. For the purposes of this disclosure, a “diastole” is the part of cardiac cycle when a chamber of heart relaxes and fills with blood. Diastolic dysfunction may be contrasted with systolic dysfunction; for the purposes of this disclosure, “systolic dysfunction” is a 36 Attorney Docket No.1518-126PCT1 dysfunction in systole where a chamber of heart is not able to contract and pump blood efficiently. For the purposes of this disclosure, a “systole” is the part of cardiac cycle when a part of a heart contracts to pump blood. For example, a left ventricular diastolic dysfunction is a dysfunction where the left ventricle of a heart cannot fully relax to fill with blood, whereas a left ventricular systolic dysfunction is a dysfunction where the left ventricle cannot efficiently pump blood into the aorta. Diastolic dysfunction is often associated with an elevated LVFP and / or pulmonary hypertension, whereas systolic dysfunction is often associated with a decrease in EF, as described above. Diastolic dysfunction may lead to diastolic heart failure, i.e., heart failure with preserved ejection fraction (HFpEF), whereas systolic dysfunction may lead to systolic heart failure, i.e., heart failure with reduced ejection fraction HFrEF) with an EF falling below 40%. With continued reference to FIG.1, in one or more embodiments, inputting cardiac input data 120 into cardiac panel 116 may comprise inputting a first plurality of time series data 140 containing the cardiac input data 120. For the purposes of this disclosure, “time series data” are data measured as a function of time and / or recorded over consistent intervals of time. In one or more embodiments, time series data may include information related to patient’s health and recorded over weeks, months, years, or decades. For example, and without limitation, time series data may include parameters such as weight, body fat, bone density, blood pressure, cholesterol levels, tobacco / alcohol consumption, substance usage, prescription dosage, or the like. In one or more embodiments, time series data may include one or more signals or parameters, such as voltage in ECG, measured using one or more medical facilities over a short time span. Time series data 140 may contain valuable information regarding patient’s journey with respect to a medical condition. Alternatively and / or additionally, time series data 140 may be used by apparatus 100 to reveal insights regarding the future trajectories of a medical condition. Additional details will be described below. With continued reference to FIG.1, in one or more embodiments, at least one cardiac models 128a-n may include a hemodynamic model configured to collect hemodynamic data and / or perform one or more hemodynamic monitoring tasks. For the purposes of this disclosure, “hemodynamic monitoring” is a procedure that checks a patient’s blood circulation and evaluates how well the patient’s heart is performing. It’s also known as a hemodynamic tilt test or a technetium hemodynamic test. Exemplary types of hemodynamic monitoring may include 37 Attorney Docket No.1518-126PCT1 electrocardiogram (ECG or EKG) monitoring, blood pressure monitoring, hemodynamic echo, nuclear medicine imaging, among others. With continued reference to FIG.1, in one or more embodiments, at least one cardiac models 128a-n may include a classification model 144 that classifies a case of diastolic dysfunction under one category of a plurality of categories, based on the severity of the case of diastolic dysfunction. As a nonlimiting example, a case of diastolic dysfunction may be categorized under one of the five categories: Normal (i.e., Grade 0), Grade 1, Grade 2, Grade 3, and Indeterminate, and the case of diastolic dysfunction may be categorized as a function of one or more cardiac indices 104a-n, or transformations therefrom, that match one category of the five categories. As another nonlimiting example, classification model 144 may be configured to generate a probability distribution for a case of diastolic dysfunction across the five categories, such as 10% Normal, 50% Grade 1, 20% Grade 2, 10% Grade 3, and 10% Indeterminate. As another nonlimiting example, classification model 144 may categorize a case of diastolic dysfunction using fuzzy set comparison and / or a fuzzy inference model. As another nonlimiting example, classification model 144 may include a binarized classification model 144, wherein a case of diastolic disfunction may be categorized as either normal or abnormal. With continued reference to FIG.1, processor 108 is further configured to generate one or more cardiac indices 104a-n from cardiac panel 116 as a function of cardiac input data 120 and at least one cardiac machine learning model 132, wherein at least one cardiac index 104 of the one or more cardiac indices 104a-n comprises a probability of patient satisfying at least one grading threshold 148. In one or more embodiments, at least one grading threshold 148 may include a grading threshold 148 in elevated LVFP. As a nonlimiting example, at least one grading threshold 148 may include a grading threshold at 16 mmHg for LVFP, beyond which the LVFP is considered elevated. In one or more embodiments, at least one grading threshold 148 may include a grading threshold 148 associated with pulmonary hypertension. As a nonlimiting example, at least one grading threshold 148 may include grading thresholds at 35 mmHg, 50 mmHg, and 70 mmHg for pulmonary artery pressure, beyond which a case of pulmonary hypertension may be classified as mild, moderate, and severe, respectively. In some cases, grading threshold 148 may include a fuzzy set, wherein determining whether the grading threshold 148 has been met or not may include a fuzzy set comparison, consistent with details described below in this disclosure. In one or more embodiments, generating one or more cardiac 38 Attorney Docket No.1518-126PCT1 indices 104a-n may include generating a second plurality of time series data 140 containing the one or more cardiac indices 104, such as LVEF, LVFP, or pulmonary artery pressure. As a nonlimiting example, processor 108 may be configured to plot one or more cardiac indices 104, such as LVEF, LVFP, and pulmonary artery pressure over a temporal span of days, weeks, months, and / or years, to track patient’s journey through one or more heart conditions such as systolic and diastolic dysfunction. Probability of patient satisfying at least one grading threshold 148 may be described in any manner consistent with cardiac index 104, as described above, including a percentage of likelihood such as 90%, a numerical value out of an arbitrary scale such as 6 / 10, or a descriptor such as “high risk” selected from a group of preset descriptors, or the like. With continued reference to FIG.1, in one or more embodiments, at least one of one or more cardiac indices 104a-n may include a cardiac index deviation 152. For the purposes of this disclosure, a “cardiac index deviation” is a parameter that indicates the change of cardiac index 104 within a preset temporal span, such as a week, a month, or a year; it is similar in nature to a first derivative of a mathematical function. Cardiac index deviation 152 may indicate how quickly patient’s heart condition or conditions improve or deteriorate over a certain time period, such as a cycle of treatment. Alternatively and / or additionally, cardiac index deviation 152 may indicate a future trajectory of its associated cardiac index 104. With continued reference to FIG.1, in one or more embodiments, generating one or more cardiac indices 104a-n may include receiving at least one user update 156 and updating at least one cardiac index 104 of the one or more cardiac indices 104a-n as a function of the at least one user update 156. In one or more embodiments, at least one user update 156 may include one or more inclusion / exclusion criteria that filter plurality cardiac indices 104a-n down to a subset thereof. In one or more embodiments, at least one user update 156 may include one or more commands that select or deselect a temporal window of interest within time series data 140 containing one or more cardiac indices 104. With continued reference to FIG.1, in one or more embodiments, processor 108 may be further configured to display at least one cardiac index 104 of one or more cardiac indices 104a-n through a user interface. In one or more embodiments, apparatus 100 may include or be coupled to a display device 160, wherein user interface is embedded within the display device 160. In some cases, user interface may include a graphical user interface. In some cases, processor 108 39 Attorney Docket No.1518-126PCT1 may be configured to generate a textual output. In some cases, processor 108 may be configured to output a .json file that may be converted to other data formats in additional downstream tasks. Additional details will be provided below in this disclosure. In one or more embodiments, generating one or more cardiac indices 104 and / or displaying at least one cardiac index 104 of the one or more cardiac indices 104a-n may include comparing the one or more cardiac indices 104a-n to one or more cardiac baselines 164, calculating one or more distance metrics 168 as a function of the comparison, and displaying the at least one cardiac index 104 of the one or more cardiac indices 104a-n as a function of at least one distance metric 168 of the one or more distance metrics 168. For the purposes of this disclosure, a “cardiac baseline” is a parameter that indicates the statistical average of cardiac indices 104a-n calculated from a large population of healthy patients. In one or more embodiments, cardiac baseline 164 may be associated with one or more statistical models, which may be described by one or more numerical indicators such as without limitation an average or mean, a median, a standard deviation, a variance, a range, or the like. In one or more embodiments, generating statistical model may include retrieving a plurality of cardiac data and / or EHRs associated with a plurality of patients from database or similar repositories. In one or more embodiments, statistical model may be updated by applying one or more inclusion / exclusion criteria, such as gender, age, weight, health condition, medical history, or the like, wherein cardiac baseline 164 may be adjusted as a function of the updated statistical model. With continued reference to FIG.1, for the purposes of this disclosure, a “distance metric” is a type of metric used in machine learning to calculate similarity between data. Common types of distance metrics may include Euclidean Distance, Manhattan Distance, Minkowski Distance, and Hamming Distance. As a nonlimiting example, a small distance metric 168 between cardiac index 104 and cardiac baseline 164 may indicate a normal cardiac index, whereas a large distance metric 168 between cardiac index 104 and cardiac baseline 164 may indicate an abnormal cardiac index. For the purposes of this disclosure, an “abnormal cardiac index” is a cardiac index 104 possessed by or associated with a minority of population and / or described by a numerical value that is different from a statistical average of the population, according to one or more cutoffs and / or pre-determined criteria. As a nonlimiting example, an abnormal cardiac index 104 may be specified as a cardiac index 104 possessed by or associated with less than 50% of the population and / or described by a numerical value that is at least two 40 Attorney Docket No.1518-126PCT1 standard deviations away from statistical average. In some cases, generating at least a distance metric 168 may include selecting one or more cutoffs, such as without limitation an absolute numerical value or a percentage, that may be used to categorize the at least a distance metric 168 into one or more categories. As a nonlimiting example, a cardiac index 104 may be classified as an outlier if its associated distance metric 168 exceeds two standard deviations compared to cardiac baseline 164, as described below. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be able to identify suitable means to implement distance metric 168 for apparatus 100. With continued reference to FIG.1, in one or more embodiments, displaying at least one cardiac index 104 of one or more cardiac indices 104a-n may further include generating a color- coded visualization 172 as a function of at least one cardiac index 104 of one or more cardiac indices 104a-n and at least one distance metric 168 of one or more distance metrics. As a nonlimiting example, a first color (e.g., green) may be used to mark a first cardiac index 104 that satisfies a first grading threshold and / or fits in a first category, a second color (e.g., yellow) may be used to mark a second cardiac index 104 that satisfies a second grading threshold and / or fits in a second category, a third color (e.g., red) may be used to mark a third cardiac index 104 that satisfies a third grading threshold and / or fits in a third category, etc. It is worth noting that the cutoffs between colors may be defined arbitrarily, as color-coded visualization may include a continuum of a plurality of colors instead. With continued reference to FIG.1, in one or more embodiments, apparatus 100 and / or processor 108 may be further configured to predict a projected cardiac index 104 as a function of cardiac input data 120 and at least one cardiac machine learning model 132, which may be implemented in any manner consistent with details described above in this disclosure. As a nonlimiting example, projected cardiac index 104 may be based on second plurality of time series data 140, as described above, or a selective subset thereof based on one or more inclusion / exclusion criteria (e.g., LVFP of the past six months), and include a projected cardiac index 104 (e.g., LVFP) 6 or 18 months later. As another nonlimiting example, projected cardiac index 104 may be presented as a risk score, such as a chance of heart failure in 6 or 18 months. Referring now to FIG.2, in one or more embodiments, cardiac input data 120 may include at least an ECG, an exemplary embodiment 200 of which is illustrated. ECG may include a plurality of features such as P-wave, Q-wave, R-wave, S-wave, QRS complex, and T wave, as 41 Attorney Docket No.1518-126PCT1 well as a plurality of parameters such a PR interval 204, QT interval 208, ST interval 212, TP interval 216, RR interval 220, and the like. P-wave may reflect atrial depolarization (activation). For the purposes of this disclosure, a “PR interval” is the distance between the onset of P-wave to the onset of QRS complex. PR interval 204 may be assessed to determine whether impulse conduction from the atria to the ventricles is normal. PR interval 204 may be measured in seconds. For the purposes of this disclosure, a “QT interval” is a reflection of the total duration of ventricular depolarization and repolarization and is measured from the onset of QRS complex to the end of T-wave. The QT duration may be inversely related to heart rate; i.e., QT interval 208 may increase at slower heart rates and decrease at higher heart rates. Therefore, to determine whether QT interval 208 is within normal limits, it may be necessary to adjust for the heart rate. A heart rate-adjusted QT interval 208 is referred to as a corrected QT interval 208 (QTc interval). A long QTc interval may indicate an increased risk of ventricular arrhythmias. The QTc interval may be in the range of 0.36 to 0.44 seconds. For the purposes of this disclosure, an “RR interval” is the time between two consecutive R waves. For the purposes of this disclosure, a “QRS complex” is a representation of the depolarization (activation) of ventricles depicted between Q-, R- and S-waves, although it may not always display all three waves. Since the electrical vector generated by the left ventricle is usually many times larger than the vector generated by the right ventricle, QRS complex is a reflection of left ventricular depolarization. With continued reference to FIG.2, for the purposes of this disclosure, an “ST interval” is the segment of ECG that starts at the end of QRS complex and extends to the beginning of T wave; it represents the early part of ventricular repolarization. ST segment may be relatively isoelectric, meaning it is at the baseline, with minimal elevation or depression. The normal duration of ST interval 212 is usually around 0.12 seconds. For the purposes of this disclosure, a “TP interval” is the segment of ECG that extends from the end of T wave to the beginning of the next P wave; it represents the time when the ventricles are fully repolarized and are in a resting state. The duration of TP interval 216 may vary but is typically short, as it may represent the brief pause between cardiac cycles. Significant deviations may be associated with certain conditions affecting repolarization. For the purposes of this disclosure, an “RR interval” is the time between two consecutive R waves of ECG; it may represent the duration of one cardiac cycle, encompassing both atrial and ventricular depolarization and repolarization. RR interval 220 may be measured in seconds and can be used to calculate heart rate (beats per minute) using 42 Attorney Docket No.1518-126PCT1ℎ^^^^^^^^ ^^^^^^^^ ൌ ^^ோோ ூ^௧^^௩^^ (in seconds). The intervals described above may be used to determine a ventricular rate, the number of ventricular contractions (heartbeats) that occur in one minute, which may be closely related to RR interval 220 of ECG, as the RR interval 220 represents the time between two consecutive ventricular contractions. Referring now to FIG.3, an exemplary embodiment of a machine learning module 300 that may perform one or more machine learning processes as described above is illustrated. Machine learning module may perform determinations, classification, and / or analysis steps, methods, processes, or the like as described in this disclosure using machine learning processes. For the purposes of this disclosure, a “machine learning process” is an automated process that uses training data 304 to generate an algorithm instantiated in hardware or software logic, data structures, and / or functions that will be performed by a computing device / module to produce outputs 308 given data provided as inputs 312; this is in contrast to a non-machine learning software program where the commands to be executed are pre-determined by user and written in a programming language. With continued reference to FIG.3, “training data”, for the purposes of this disclosure, are data containing correlations that a machine learning process may use to model relationships between two or more categories of data elements. For instance, and without limitation, training data 304 may include a plurality of data entries, also known as “training examples”, each entry representing a set of data elements that were recorded, received, and / or generated together. Data elements may be correlated by shared existence in a given data entry, by proximity in a given data entry, or the like. Multiple data entries in training data 304 may evince one or more trends in correlations between categories of data elements; for instance, and without limitation, a higher value of a first data element belonging to a first category of data element may tend to correlate to a higher value of a second data element belonging to a second category of data element, indicating a possible proportional or other mathematical relationship linking values belonging to the two categories. Multiple categories of data elements may be related in training data 304 according to various correlations; correlations may indicate causative and / or predictive links between categories of data elements, which may be modeled as relationships such as mathematical relationships by machine learning processes as described in further detail below. Training data 304 may be formatted and / or organized by categories of data elements, for instance by associating data elements with one or more descriptors corresponding to categories of data 43 Attorney Docket No.1518-126PCT1 elements. As a nonlimiting example, training data 304 may include data entered in standardized forms by persons or processes, such that entry of a given data element within a given field in a given form may be mapped to one or more descriptors of categories. Elements in training data 304 may be linked to descriptors of categories by tags, tokens, or other data elements. For instance, and without limitation, training data 304 may be provided in fixed-length formats, formats linking positions of data to categories such as comma-separated value (CSV) formats and / or self-describing formats such as extensible markup language (XML), JavaScript Object Notation (JSON), or the like, enabling processes or devices to detect categories of data. With continued reference to FIG.3, alternatively or additionally, training data 304 may include one or more elements that are uncategorized; that is, training data 304 may not be formatted or contain descriptors for some elements of data. Machine learning algorithms and / or other processes may sort training data 304 according to one or more categorizations using, for instance, natural language processing algorithms, tokenization, detection of correlated values in raw data, and the like; categories may be generated using correlation and / or other processing algorithms. As a nonlimiting example, in a corpus of text, phrases making up a number “n” of compound words, such as nouns modified by other nouns, may be identified according to a statistically significant prevalence of n-grams containing such words in a particular order; such an n-gram may be categorized as an element of language such as a “word” to be tracked similarly to single words, generating a new category as a result of statistical analysis. Similarly, in a data entry including some textual data, a person’s name may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine learning algorithms, and / or automated association of data in the data entry with descriptors or into a given format. The ability to categorize data entries automatedly may enable the same training data 304 to be made applicable for two or more distinct machine learning algorithms as described in further detail below. Training data 304 used by machine learning module 300 may correlate any input data as described in this disclosure to any output data as described in this disclosure. As a nonlimiting illustrative example, inputs may include plurality of cardiac input data 120, whereas outputs may include plurality of cardiac indices 104. With continued reference to FIG.3, training data 304 may be filtered, sorted, and / or selected using one or more supervised and / or unsupervised machine learning processes and / or models as described in further detail below; such processes and / or models may include without 44 Attorney Docket No.1518-126PCT1 limitation a training data classifier 316. For the purposes of this disclosure, a “classifier” is a machine learning model, such as a data structure representing and / or using a mathematical model, neural net, or a program generated by a machine learning algorithm, known as a “classification algorithm,” that sorts inputs into categories or bins of data, outputting the categories or bins of data and / or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. A distance metric may include any norm, such as, without limitation, a Pythagorean norm. Machine learning module 300 may generate a classifier using a classification algorithm. For the purposes of this disclosure, a “classification algorithm” is a process wherein a computing device and / or any module and / or component operating therein derives a classifier from training data 304. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and / or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, Fisher’s linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and / or neural network-based classifiers. In one or more embodiments, training data classifier 316 may classify elements of training data to a plurality of cohorts as a function of certain anatomic and / or demographic traits. With continued reference to FIG.3, machine learning module 300 may be configured to generate a classifier using a naive Bayes classification algorithm. Naive Bayes classification algorithm generates classifiers by assigning class labels to problem instances, represented as vectors of element values. Class labels are drawn from a finite set. Naive Bayes classification algorithm may include generating a family of algorithms that assume that the value of a particular element is independent of the value of any other element, given a class variable. NaiveBayes classification algorithm may be based on Bayes Theorem expressed as ^^^^^ / ^^^ ൌ^^^^^ / ^^^ ൈ ^^^^^^ ൊ ^^^^^^, where ^^^^^ / ^^^ is the probability of hypothesis ^^ given data ^^, alsoknown as posterior probability; ^^^^^ / ^^^ is the probability of data ^^ given that the hypothesis ^^ was true; ^^^^^^ is the probability of hypothesis ^^ being true regardless of data, also known as prior probability of ^^; and ^^^^^^ is the probability of the data regardless of the hypothesis. A naive Bayes algorithm may be generated by first transforming training data into a frequency table. Machine learning module 300 may then calculate a likelihood table by calculating 45 Attorney Docket No.1518-126PCT1 probabilities of different data entries and classification labels. Machine learning module 300 may utilize a naive Bayes equation to calculate a posterior probability for each class. A class containing the highest posterior probability is the outcome of prediction. Naive Bayes classification algorithm may include a gaussian model that follows a normal distribution. Naive Bayes classification algorithm may include a multinomial model that is used for discrete counts. Naive Bayes classification algorithm may include a Bernoulli model that may be utilized when vectors are binary. With continued reference to FIG.3, machine learning module 300 may be configured to generate a classifier using a k-nearest neighbors (KNN) algorithm. For the purposes of this disclosure, a “k-nearest neighbors algorithm” is or at least includes a classification method that utilizes feature similarity to analyze how closely out-of-sample features resemble training data 304 and to classify input data to one or more clusters and / or categories of features as represented in training data 304; this may be performed by representing both training data 304 and input data in vector forms and using one or more measures of vector similarity to identify classifications within training data 304 and determine a classification of input data. K-nearest neighbors algorithm may include specifying a k-value, or a number directing the classifier to select the k most similar entries of training data 304 to a given sample, determining the most common classifier of the entries in the database, and classifying the known sample; this may be performed recursively and / or iteratively to generate a classifier that may be used to classify input data as further samples. For instance, an initial set of samples may be performed to cover an initial heuristic and / or “first guess” at an output and / or relationship, which may be seeded, without limitation, using expert input received according to any process as described herein. As a nonlimiting example, an initial heuristic may include a ranking of associations between inputs 312 and elements of training data 304. Heuristic may include selecting some number of highest- ranking associations and / or training data elements. With continued reference to FIG.3, generating k-nearest neighbors algorithm may generate a first vector output containing a data entry cluster, generating a second vector output containing input data, and calculate the distance between the first vector output and the second vector output using any suitable norm such as cosine similarity, Euclidean distance measurement, or the like. Each vector output may be represented, without limitation, as an n- tuple of values, where n is at least 2. Each value of n-tuple of values may represent a 46 Attorney Docket No.1518-126PCT1 measurement or other quantitative value associated with a given category of data or attribute, examples of which are provided in further detail below. A vector may be represented, without limitation, in n-dimensional space using an axis per category of value represented in n-tuple of values, such that a vector has a geometric direction characterizing the relative quantities of attributes in the n-tuple as compared to each other. Two vectors may be considered equivalent when their directions and / or relative quantities of values are the same; thus, as a nonlimiting example, a vector represented as [5, 10, 15] may be treated as equivalent, for the purposes of this disclosure, as a vector represented as [1, 2, 3]. Vectors may be more similar where their directions are more similar, and more different where their directions are more divergent. However, vector similarity may alternatively or additionally be determined using averages of similarities between like attributes, or any other measure of similarity suitable for any n-tuple of values, or aggregation of numerical similarity measures for the purposes of loss functions as described in further detail below. Any vectors as described herein may be scaled, such that each vector represents each attribute along an equivalent scale of values. Each vector may be “normalized,” or divided by a “length” attribute, such as a length attribute l as derived using aPythagorean norm: ^^ ൌ ^∑^^ୀ^ ^^^ଶ , where ^^^ is attribute number of vector ^^. Scaling and / ornormalization may vector comparison independent of absolute quantities of attributes, while preserving any dependency on similarity of attributes. This may, for instance, be advantageous where cases represented in training data 304 are represented by different quantities of samples, which may result in proportionally equivalent vectors with divergent values. With continued reference to FIG.3, training examples for use as training data may be selected from a population of potential examples according to cohorts relevant to an analytical problem to be solved, a classification task, or the like. Alternatively or additionally, training data 304 may be selected to span a set of likely circumstances or inputs for a machine learning model and / or process to encounter when deployed. For instance, and without limitation, for each category of input data to a machine learning model and / or process that may exist in a range of values in a population of phenomena such as images, user data, process data, physical data, or the like, a computing device, processor 108, and / or machine learning module 300 may select training examples representing each possible value on such a range and / or a representative sample of values on such a range. Selection of a representative sample may include selection of training examples in proportions matching a statistically determined and / or predicted distribution 47 Attorney Docket No.1518-126PCT1 of such values according to relative frequency, such that, for instance, values encountered more frequently in a population of data so analyzed are represented by more training examples than values that are encountered less frequently. Alternatively or additionally, a set of training examples may be compared to a collection of representative values in a database and / or presented to user, so that a process can detect, automatically or via user input, one or more values that are not included in the set of training examples. Computing device, processor 108, and / or machine learning module 300 may automatically generate a missing training example. This may be done by receiving and / or retrieving a missing input and / or output value and correlating the missing input and / or output value with a corresponding output and / or input value collocated in a data record with the retrieved value, provided by user, another device, or the like. With continued reference to FIG.3, computing device, processor 108, and / or machine learning module 300 may be configured to preprocess training data 304. For the purposes of this disclosure, “preprocessing” training data is a process that transforms training data from a raw form to a format that can be used for training a machine learning model. Preprocessing may include sanitizing, feature selection, feature scaling, data augmentation and the like. With continued reference to FIG.3, computing device, processor 108, and / or machine learning module 300 may be configured to sanitize training data. For the purposes of this disclosure, “sanitizing” training data is a process whereby training examples that interfere with convergence of a machine learning model and / or process are removed to yield a useful result. For instance, and without limitation, a training example may include an input and / or output value that is an outlier from typically encountered values, such that a machine learning algorithm using the training example will be skewed to an unlikely range of input 312 and / or output 308; a value that is more than a threshold number of standard deviations away from an average, mean, or expected value, for instance, may be eliminated. Alternatively or additionally, one or more training examples may be identified as having poor-quality data, where “poor-quality” means having a signal-to-noise ratio below a threshold value. In one or more embodiments, sanitizing training data may include steps such as removing duplicative or otherwise redundant data, interpolating missing data, correcting data errors, standardizing data, identifying outliers, and / or the like. In one or more embodiments, sanitizing training data may include algorithms that identify duplicate entries or spell-check algorithms. With continued reference to FIG.3, in one or more embodiments, images used to train an 48 Attorney Docket No.1518-126PCT1 image classifier or other machine learning model and / or process that takes images as inputs 312 or generates images as outputs 308 may be rejected if image quality is below a threshold value. For instance, and without limitation, computing device, processor 108, and / or machine learning module 300 may perform blur detection. Elimination of one or more blurs may be performed, as a nonlimiting example, by taking Fourier transform or a Fast Fourier Transform (FFT) of image and analyzing a distribution of low and high frequencies in the resulting frequency-domain depiction of the image. Numbers of high-frequency values below a threshold level may indicate blurriness. As a further nonlimiting example, detection of blurriness may be performed by convolving an image, a channel of an image, or the like with a Laplacian kernel; this may generate a numerical score reflecting a number of rapid changes in intensity shown in the image, such that a high score indicates clarity and a low score indicates blurriness. Blurriness detection may be performed using a gradient-based operator, which measures operators based on the gradient or first derivative of image, based on the hypothesis that rapid changes indicate sharp edges in the image, and thus are indicative of a lower degree of blurriness. Blur detection may be performed using a wavelet-based operator, which uses coefficients of a discrete wavelet transform to describe the frequency and spatial content of images. Blur detection may be performed using statistics-based operators that take advantage of several image statistics as texture descriptors in order to compute a focus level. Blur detection may be performed by using discrete cosine transform (DCT) coefficients in order to compute a focus level of an image from its frequency content. With continued reference to FIG.3, computing device, processor 108, and / or machine learning module 300 may be configured to precondition one or more training examples. For instance, and without limitation, where a machine learning model and / or process has one or more inputs 312 and / or outputs 308 requiring, transmitting, or receiving a certain number of bits, samples, or other units of data, one or more elements of training examples to be used as or compared to inputs 312 and / or outputs 308 may be modified to have such a number of units of data. In one or more embodiments, computing device, processor 108, and / or machine learning module 300 may convert a smaller number of units, such as in a low pixel count image, into a desired number of units by upsampling and interpolating. As a nonlimiting example, a low pixel count image may have 100 pixels, whereas a desired number of pixels may be 128. Processor 108 may interpolate the low pixel count image to convert 100 pixels into 128 pixels. It should 49 Attorney Docket No.1518-126PCT1 also be noted that one of ordinary skill in the art, upon reading the entirety of this disclosure, would recognize the various methods to interpolate a smaller number of data units such as samples, pixels, bits, or the like to a desired number of such units. In one or more embodiments, a set of interpolation rules may be trained by sets of highly detailed inputs 312 and / or outputs 308 and corresponding inputs 312 and / or outputs 308 downsampled to smaller numbers of units, and a neural network or another machine learning model that is trained to predict interpolated pixel values using the training data 304. As a nonlimiting example, a sample input 312 and / or output 308, such as a sample picture, with sample-expanded data units (e.g., pixels added between the original pixels) may be input to a neural network or machine learning model and output a pseudo replica sample picture with dummy values assigned to pixels between the original pixels based on a set of interpolation rules. As a nonlimiting example, in the context of an image classifier, a machine learning model may have a set of interpolation rules trained by sets of highly detailed images and images that have been downsampled to smaller numbers of pixels, and a neural network or other machine learning model that is trained using those examples to predict interpolated pixel values in a facial picture context. As a result, an input with sample-expanded data units (the ones added between the original data units, with dummy values) may be run through a trained neural network and / or model, which may fill in values to replace the dummy values. Alternatively or additionally, computing device, processor 108, and / or machine learning module 300 may utilize sample expander methods, a low-pass filter, or both. For the purposes of this disclosure, a “low-pass filter” is a filter that passes signals with a frequency lower than a selected cutoff frequency and attenuates signals with frequencies higher than the cutoff frequency. The exact frequency response of the filter depends on the filter design. Computing device, processor 108, and / or machine learning module 300 may use averaging, such as luma or chroma averaging in images, to fill in data units in between original data units. With continued reference to FIG.3, in one or more embodiments, computing device, processor 108, and / or machine learning module 300 may downsample elements of a training example to a desired lower number of data elements. As a nonlimiting example, a high pixel count image may contain 256 pixels, however a desired number of pixels may be 128. Processor 108 may downsample the high pixel count image to convert 256 pixels into 128 pixels. In one or more embodiments, processor 108 may be configured to perform downsampling on data. Downsampling, also known as decimation, may include removing every Nthentry in a sequence 50 Attorney Docket No.1518-126PCT1 of samples, all but every Nthentry, or the like, which is a process known as “compression” and may be performed, for instance by an N-sample compressor implemented using hardware or software. Anti-aliasing and / or anti-imaging filters, and / or low-pass filters, may be used to eliminate side effects of compression. With continued reference to FIG.3, feature selection may include narrowing and / or filtering training data 304 to exclude features and / or elements, or training data including such elements that are not relevant to a purpose for which a trained machine learning model and / or algorithm is being trained, and / or collection of features, elements, or training data including such elements based on relevance to or utility for an intended task or purpose for which a machine learning model and / or algorithm is being trained. Feature selection may be implemented, without limitation, using any process described in this disclosure, including without limitation using training data classifiers, exclusion of outliers, or the like. With continued reference to FIG.3, feature scaling may include, without limitation, normalization of data entries, which may be accomplished by dividing numerical fields by norms thereof, for instance as performed for vector normalization. Feature scaling may include absolute maximum scaling, wherein each quantitative datum is divided by the maximum absolute value of all quantitative data of a set or subset of quantitative data. Feature scaling may include min-max scaling, wherein a difference between each value, ^^, and a minimum value, ^^^^^, in a set orsubset of values is divided by a range of values, ^^^^௫ െ ^^^^^, in the set or subset: ^^^^௪ ൌ^ି^^^^^^ೌ^ି^^^^. Feature scaling may include mean normalization, wherein a each value, X, and a mean value of a set and / or subset of values, ^^^^^^, is divided by a range ofvalues, ^^^^௫ െ ^^^^^, in the set or subset: ^^^^௪ ൌ^ି^^^ೌ^^^ೌ^ି^^^^. Feature scaling may include standardization, wherein a difference between ^^ and ^^^^^^is divided by a standard deviation, ^^, of a set or subset of values: ^^^ି^^^ೌ^^^௪ൌ . Feature scaling may be performed using a median value of a set or subset, X , range (IQR), which represents difference between the 25thpercentile value and the 50thpercentile value (or closest values thereto by a rounding protocol), such as: ^^^ି^^^^^ೌ^^^௪ൌ ூொோ . A Person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be of various alternative or additional approaches that may be used for feature scaling. With continued reference to FIG.3, computing device, processor 108, and / or machine 51 Attorney Docket No.1518-126PCT1 learning module 300 may be configured to perform one or more processes of data augmentation. For the purposes of this disclosure, “data augmentation” is a process that adds data to a training data 304 using elements and / or entries already in the dataset. Data augmentation may be accomplished, without limitation, using interpolation, generation of modified copies of existing entries and / or examples, and / or one or more generative artificial intelligence (AI) processes, for instance using deep neural networks and / or generative adversarial networks. Generative processes may be referred to alternatively in this context as “data synthesis” and as creating “synthetic data.” Augmentation may include performing one or more transformations on data, such as geometric, color space, affine, brightness, cropping, and / or contrast transformations of images. With continued reference to FIG.3, machine learning module 300 may be configured to perform a lazy learning process and / or protocol 320. For the purposes of this disclosure, a “lazy learning” process and / or protocol is a process whereby machine learning is conducted upon receipt of input 312 to be converted to output 308 by combining the input 312 and training data 304 to derive the algorithm to be used to produce the output 308 on demand. A lazy learning process may alternatively be referred to as a “lazy loading” or “call-when-needed” process and / or protocol. For instance, an initial set of simulations may be performed to cover an initial heuristic and / or “first guess” at an output 308 and / or relationship. As a nonlimiting example, an initial heuristic may include a ranking of associations between inputs 312 and elements of training data 304. Heuristic may include selecting some number of highest-ranking associations and / or training data 304 elements. Lazy learning may implement any suitable lazy learning algorithm, including without limitation a k-nearest neighbors algorithm, a lazy naive Bayes algorithm, or the like. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various lazy learning algorithms that may be applied to generate outputs as described in this disclosure, including without limitation lazy learning applications of machine learning algorithms as described in further detail below. With continued reference to FIG.3, alternatively or additionally, machine learning processes as described in this disclosure may be used to generate machine learning models 324. A “machine learning model,” for the purposes of this disclosure, is a data structure representing and / or instantiating a mathematical and / or algorithmic representation of a relationship between inputs 312 and outputs 308, generated using any machine learning process including without 52 Attorney Docket No.1518-126PCT1 limitation any process described above, and stored in memory. An input 312 is submitted to a machine learning model 324 once created, which generates an output 308 based on the relationship that was derived. For instance, and without limitation, a linear regression model, generated using a linear regression algorithm, may compute a linear combination of input data using coefficients derived during machine learning processes to calculate an output datum. As a further nonlimiting example, a machine learning model 324 may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created by "training" the network, in which elements from a training data 304 are applied to the input nodes, and a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning, as described in detail below. With continued reference to FIG.3, machine learning module 300 may perform at least a supervised machine learning process 328. For the purposes of this disclosure, a “supervised” machine learning process is a process with algorithms that receive training data 304 relating one or more inputs 312 to one or more outputs 308, and seek to generate one or more data structures representing and / or instantiating one or more mathematical relations relating input 312 to output 308, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include inputs 312 described above as inputs, and outputs 308 described above as outputs, and a scoring function representing a desired form of relationship to be detected between inputs 312 and outputs 308. Scoring function may, for instance, seek to maximize the probability that a given input 312 and / or combination thereof is associated with a given output 308 to minimize the probability that a given input 312 is not associated with a given output 308. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs 312 to outputs 308, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data 304. Supervised machine learning processes may include classification algorithms as defined above. A person of ordinary skill in the art, upon 53 Attorney Docket No.1518-126PCT1 reviewing the entirety of this disclosure, will be aware of various possible variations of at least a supervised machine learning process 328 that may be used to determine a relation between inputs and outputs. With continued reference to FIG.3, training a supervised machine learning process may include, without limitation, iteratively updating coefficients, biases, and weights based on an error function, expected loss, and / or risk function. For instance, an output 308 generated by a supervised machine learning process 328 using an input example in a training example may be compared to an output example from the training example; an error function may be generated based on the comparison, which may include any error function suitable for use with any machine learning algorithm described in this disclosure, including a square of a difference between one or more sets of compared values or the like. Such an error function may be used in turn to update one or more weights, biases, coefficients, or other parameters of a machine learning model through any suitable process including without limitation gradient descent processes, least-squares processes, and / or other processes described in this disclosure. This may be done iteratively and / or recursively to gradually tune such weights, biases, coefficients, or other parameters. Updates may be performed in neural networks using one or more back- propagation algorithms. Iterative and / or recursive updates to weights, biases, coefficients, or other parameters as described above may be performed until currently available training data 304 are exhausted and / or until a convergence test is passed. For the purposes of this disclosure, a “convergence test” is a test for a condition selected to indicate that a model and / or weights, biases, coefficients, or other parameters thereof has reached a degree of accuracy. A convergence test may, for instance, compare a difference between two or more successive errors or error function values, where differences below a threshold amount may be taken to indicate convergence. Alternatively or additionally, one or more errors and / or error function values evaluated in training iterations may be compared to a threshold. With continued reference to FIG.3, a computing device, processor 108, and / or machine learning module 300 may be configured to perform method, method step, sequence of method steps, and / or algorithm described in reference to this figure, in any order and with any degree of repetition. For instance, computing device, processor 108, and / or machine learning module 300 may be configured to perform a single step, sequence, and / or algorithm repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed 54 Attorney Docket No.1518-126PCT1 iteratively and / or recursively using outputs 308 of previous repetitions as inputs 312 to subsequent repetitions, aggregating inputs 312 and / or outputs 308 of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and / or division of a larger processing task into a set of iteratively addressed smaller processing tasks. A computing device, processor 108, apparatus 100, or machine learning module 300 may perform any step, sequence of steps, or algorithm in parallel, such as simultaneously and / or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and / or processes may be performed according to any protocol suitable for division of tasks between iterations. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and / or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and / or parallel processing. With continued reference to FIG.3, machine learning process may include at least an unsupervised machine learning process 332. For the purposes of this disclosure, an unsupervised machine learning process is a process that derives inferences in datasets without regard to labels. As a result, an unsupervised machine learning process 332 may be free to discover any structure, relationship, and / or correlation provided in the data. Unsupervised processes 332 may not require a response variable, may be used to find interesting patterns and / or inferences between variables, to determine a degree of correlation between two or more variables, or the like. With continued reference to FIG.3, machine learning module 300 may be designed and configured to create machine learning model 324 using techniques for development of linear regression models. Linear regression models may include ordinary least squares regression, which aims to minimize the square of the difference between predicted outcomes and actual outcomes according to an appropriate norm for measuring such a difference (e.g. a vector-space distance norm); coefficients of the resulting linear equation may be modified to improve minimization. Linear regression models may include ridge regression methods, where the function to be minimized includes the least-squares function plus term multiplying the square of each coefficient by a scalar amount to penalize large coefficients. Linear regression models may include least absolute shrinkage and selection operator (LASSO) models, in which ridge regression is combined with multiplying the least-squares term by a factor of 1 divided by double the number of samples. Linear regression models may include a multi-task lasso model wherein 55 Attorney Docket No.1518-126PCT1 the norm applied in the least-squares term of the lasso model is the Frobenius norm amounting to the square root of the sum of squares of all terms. Linear regression models may include an elastic net model, a multi-task elastic net model, a least angle regression model, a LARS lasso model, an orthogonal matching pursuit model, a Bayesian regression model, a logistic regression model, a stochastic gradient descent model, a perceptron model, a passive aggressive algorithm, a robustness regression model, a Huber regression model, or any other suitable model that may occur to a person of ordinary skill in the art upon reviewing the entirety of this disclosure. Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher-order equation) providing a best predicted output / actual output fit is sought. Similar methods to those described above may be applied to minimize error functions, as will be apparent to a person of ordinary skill in the art upon reviewing the entirety of this disclosure. With continued reference to FIG.3, machine learning algorithms may include, without limitation, linear discriminant analysis. Machine learning algorithm may include quadratic discriminant analysis. Machine learning algorithms may include kernel ridge regression. Machine learning algorithms may include support vector machines, including without limitation support vector classification-based regression processes. Machine learning algorithms may include stochastic gradient descent algorithms, including classification and regression algorithms based on stochastic gradient descent. Machine learning algorithms may include nearest neighbors algorithms. Machine learning algorithms may include various forms of latent space regularization such as variational regularization. Machine learning algorithms may include Gaussian processes such as Gaussian Process Regression. Machine learning algorithms may include cross-decomposition algorithms, including partial least squares and / or canonical correlation analysis. Machine learning algorithms may include naive Bayes methods. Machine learning algorithms may include algorithms based on decision trees, such as decision tree classification or regression algorithms. Machine learning algorithms may include ensemble methods such as bagging meta-estimator, forest of randomized trees, AdaBoost, gradient tree boosting, and / or voting classifier methods. Machine learning algorithms may include neural net algorithms, including convolutional neural net processes. With continued reference to FIG.3, a machine learning model and / or process may be deployed or instantiated by incorporation into a program, apparatus, system, and / or module. For 56 Attorney Docket No.1518-126PCT1 instance, and without limitation, a machine learning model, neural network, and / or some or all parameters thereof may be stored and / or deployed in any memory or circuitry. Parameters such as coefficients, weights, and / or biases may be stored as circuit-based constants, such as arrays of wires and / or binary inputs and / or outputs set at logic “1” and “0” voltage levels in a logic circuit, to represent a number according to any suitable encoding system including twos complement or the like, or may be stored in any volatile and / or non-volatile memory. Similarly, mathematical operations and input 312 and / or output 308 of data to or from models, neural network layers, or the like may be instantiated in hardware circuitry and / or in the form of instructions in firmware, machine-code such as binary operation code instructions, assembly language, or any higher- order programming language. Any technology for hardware and / or software instantiation of memory, instructions, data structures, and / or algorithms may be used to instantiate a machine learning process and / or model, including without limitation any combination of production and / or configuration of non-reconfigurable hardware elements, circuits, and / or modules such as without limitation application-specific integrated circuits (ASICs), production and / or configuration of reconfigurable hardware elements, circuits, and / or modules such as without limitation field programmable gate arrays (FPGAs), production and / or configuration of non- reconfigurable and / or non-rewritable memory elements, circuits, and / or modules such as without limitation non-rewritable read-only memory (ROM), other memory technology described in this disclosure, and / or production and / or configuration of any computing device and / or component thereof as described in this disclosure. Such deployed and / or instantiated machine learning model and / or algorithm may receive inputs 312 from any other process, module, and / or component described in this disclosure, and produce outputs 308 to any other process, module, and / or component described in this disclosure. With continued reference to FIG.3, any process of training, retraining, deployment, and / or instantiation of any machine learning model and / or algorithm may be performed and / or repeated after an initial deployment and / or instantiation to correct, refine, and / or improve the machine learning model and / or algorithm. Such retraining, deployment, and / or instantiation may be performed as a periodic or regular process, such as retraining, deployment, and / or instantiation at regular elapsed time periods, after some measure of volume such as a number of bytes or other measures of data processed, a number of uses or performances of processes described in this disclosure, or the like, and / or according to a software, firmware, or other update 57 Attorney Docket No.1518-126PCT1 schedule. Alternatively or additionally, retraining, deployment, and / or instantiation may be event-based, and may be triggered, without limitation, by user inputs indicating sub-optimal or otherwise problematic performance and / or by automated field testing and / or auditing processes, which may compare outputs 308 of machine learning models and / or algorithms, and / or errors and / or error functions thereof, to any thresholds, convergence tests, or the like, and / or may compare outputs 308 of processes described herein to similar thresholds, convergence tests or the like. Event-based retraining, deployment, and / or instantiation may alternatively or additionally be triggered by receipt and / or generation of one or more new training examples; a number of new training examples may be compared to a preconfigured threshold, where exceeding the preconfigured threshold may trigger retraining, deployment, and / or instantiation. With continued reference to FIG.3, retraining and / or additional training may be performed using any process for training described above, using any currently or previously deployed version of a machine learning model and / or algorithm as a starting point. Training data for retraining may be collected, preconditioned, sorted, classified, sanitized, or otherwise processed according to any process described in this disclosure. Training data 304 may include, without limitation, training examples including inputs 312 and correlated outputs 308 used, received, and / or generated from any version of any system, module, machine learning model or algorithm, apparatus, and / or method described in this disclosure. Such examples may be modified and / or labeled according to user feedback or other processes to indicate desired results, and / or may have actual or measured results from a process being modeled and / or predicted by system, module, machine learning model or algorithm, apparatus, and / or method as “desired” results to be compared to outputs 308 for training processes as described above. Redeployment may be performed using any reconfiguring and / or rewriting of reconfigurable and / or rewritable circuit and / or memory elements; alternatively, redeployment may be performed by production of new hardware and / or software components, circuits, instructions, or the like, which may be added to and / or may replace existing hardware and / or software components, circuits, instructions, or the like. With continued reference to FIG.3, one or more processes or algorithms described above may be performed by at least a dedicated hardware unit 336. For the purposes of this disclosure, a “dedicated hardware unit” is a hardware component, circuit, or the like, aside from a principal control circuit and / or processor 108 performing method steps as described in this disclosure, that 58 Attorney Docket No.1518-126PCT1 is specifically designated or selected to perform one or more specific tasks and / or processes described in reference to this figure, such as without limitation preprocessing and / or sanitization of training data and / or training a machine learning algorithm and / or model. Dedicated hardware unit 336 may include, without limitation, a hardware unit that can perform iterative or massed calculations, such as matrix-based calculations to update or tune parameters, weights, coefficients, and / or biases of machine learning models and / or neural networks, efficiently using pipelining, parallel processing, or the like; such a hardware unit may be optimized for such processes by, for instance, including dedicated circuitry for matrix and / or signal processing operations that includes, e.g., multiple arithmetic and / or logical circuit units such as multipliers and / or adders that can act simultaneously, in parallel, and / or the like. Such dedicated hardware units 336 may include, without limitation, graphical processing units (GPUs), dedicated signal processing modules, field programmable gate arrays (FPGA), other reconfigurable hardware that has been configured to instantiate parallel processing units for one or more specific tasks, or the like. Computing device, processor 108, apparatus 100, or machine learning module 300 may be configured to instruct one or more dedicated hardware units 336 to perform one or more operations described herein, such as evaluation of model and / or algorithm outputs, one-time or iterative updates to parameters, coefficients, weights, and / or biases, vector and / or matrix operations, and / or any other operations described in this disclosure. Referring now to FIG.4, an exemplary embodiment of neural network 400 is illustrated. For the purposes of this disclosure, a neural network or artificial neural network is a network of “nodes” or data structures having one or more inputs, one or more outputs, and a function determining outputs based on inputs. Such nodes may be organized in a network, such as without limitation a convolutional neural network, including an input layer of nodes 404, at least an intermediate layer of nodes 408, and an output layer of nodes 412. Connections between nodes may be created via the process of "training" neural network 400, in which elements from a training dataset are applied to the input nodes, and a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network 400 to produce the desired values at the output nodes. This process is sometimes referred to as deep learning. Connections may run solely from input nodes toward output nodes in a “feed-forward” network or may feed outputs of one layer back to inputs of the same or a different layer in a 59 Attorney Docket No.1518-126PCT1 “recurrent network.” As a further nonlimiting example, neural network 400 may include a convolutional neural network comprising an input layer of nodes 404, one or more intermediate layers of nodes 408, and an output layer of nodes 412. For the purposes of this disclosure, a “convolutional neural network” is a type of neural network 400 in which at least one hidden layer is a convolutional layer that convolves inputs to that layer with a subset of inputs known as a “kernel”, along with one or more additional layers such as pooling layers, fully connected layers, and the like. Referring now to FIG.5, an exemplary embodiment of a node 500 of neural network 400 is illustrated. Node 500 may include, without limitation, a plurality of inputs, ^^^, that may receive numerical values from inputs to neural network 400 containing the node 500 and / or from other nodes 500. Node 500 may perform one or more activation functions to produce its output given one or more inputs, such as without limitation computing a binary step function comparing an input to a threshold value and outputting either a logic 1 or logic 0 output or its equivalent, a linear activation function whereby an output is directly proportional to input, and / or a nonlinear activation function wherein the output is not proportional to the input. Nonlinear activationfunctions may include, without limitation, a sigmoid function of the form ^^^^^^ ൌ ^^ି^ష^given ^^ି^ష^ input ^^, a tanh (hyperbolic tangent) function of the form^^ା^ష^, a tanh derivative suchas ^^^^^^ ൌ ^^^^^^ℎଶ^^^^, a rectified linear unit function such as ^^^^^^ ൌ ^^^^^^ ^0, ^^^, a “leaky” and / or“parametric” rectified linear unit function such as ^^^^^^ ൌ ^^^^^^ ^^^^^, ^^^ for some value of ^^, anexponential linear units function such as ^^^^^^ ൌ ^ ^^ ^^^^^^ ^^ ^ 0^^^^^௫ െ 1^ ^^^^^^ ^^ ^ 0for some value of ^^ (this function may be replaced and / or weighted by its own derivative in some embodiments), asoftmax function such as ^^^^^^^ ൌ ^^∑^ ௫^where the inputs to an instant layer are ^^^, a swishfunction such as ^^^^^^ ൌ ^^ ∗ a Gaussian error linear unit function such as ^^^^^^ ൌ^^൫1 ^ ^^^^^^ℎ ^^2 / ^^^^^ ^ ^^^^^^^൯ for some values of ^^, ^^, and ^^, and / or a scaled exponentialൌ ^^ ^ ^^^^^௫ െ 1^ ^^^^^^ ^^ ^ 0^^ ^^^^^^ ^^ ^. Fundamentally, there is no limit to ^^^, functions. As a nonlimiting and illustrative example, node 500 may perform a weighted sum of inputs using weights, ^^^, that are multiplied by respective inputs, ^^^,. Additionally or alternatively, a bias ^^ may be added to 60 Attorney Docket No.1518-126PCT1 the weighted sum of the inputs such that an offset is added to each unit in a neural network layer that is independent of the input to the layer. The weighted sum may then be input into a function, ^^, which may generate one or more outputs, ^^. Weight, ^^^, applied to an input, ^^^, may indicate whether the input is “excitatory”, indicating that it has strong influence on the one or more outputs, ^^, for instance by the corresponding weight having a large numerical value, or “inhibitory”, indicating it has a weak influence on the one more outputs, y, for instance by the corresponding weight having a small numerical value. The values of weights, ^^^, may be determined by training neural network 400 using training data, which may be performed using any suitable process as described above. Referring now to FIG.6, an exemplary embodiment of fuzzy set comparison 600 is illustrated. A first fuzzy set 604 may be represented, without limitation, according to a first membership function 608 representing a probability that an input falling on a first range of values 612 is a member of the first fuzzy set 604, where the first membership function 608 has values on a range of probabilities such as without limitation the interval [0,1], and an area beneath the first membership function 608 may represent a set of values within the first fuzzy set 604. Although first range of values 612 is illustrated for clarity in this exemplary depiction as a range on a single number line or axis, first range of values 612 may be defined on two or more dimensions, representing, for instance, a Cartesian product between a plurality of ranges, curves, axes, spaces, dimensions, or the like. First membership function 608 may include any suitable function mapping first range of values 612 to a probability interval, including without limitation a triangular function defined by two linear elements such as line segments or planes that intersect at or below the top of the probability interval. As a non-limiting example, triangular membership function may be defined as: ì0,^^^^^^ ^^ ^ ^^ ^^^^^^ ^^ ^ ^^^^ ^^a trapezoidal ^^ െ ^^ ^^ െ ^^^^^^^^ ^ 0^a sigmoidal 61 Attorney Docket No.1518-126PCT1^ ^1 ^^ ^^,^^, ^^ ൌ1 െ ^^ି^^௫ି^^a Gaussian membership ^^^^^, ^^, ^^^ ൌ ^^ ଶ ఙand a bell membership function may be defined as: ^, ^^, ^^, ൌ ^1 ^ ^ ^^ െ ^^ଶ^ ି^ ^^^^^, ^ ^^ ൨ ^^ A person of ordinary entirety of this disclosure, will be aware of various alternative or that may be used consistently with this disclosure. With continued reference to FIG.6, in one or more embodiments, first fuzzy set 604 may represent any value or combination of values as described above, including output from one or more machine learning models. A second fuzzy set 616, which may represent any value which may be represented by first fuzzy set 604, may be defined by a second membership function 620 on a second range 624; second range 624 may be identical and / or overlap with first range of values 612 and / or may be combined with first range via Cartesian product or the like to generate a mapping permitting evaluation overlap of first fuzzy set 604 and second fuzzy set 616. Where first fuzzy set 604 and second fuzzy set 616 have a region 628 that overlaps, first membership function 608 and second membership function 620 may intersect at a point 632 representing a probability, as defined on probability interval, of a match between first fuzzy set 604 and second fuzzy set 616. Alternatively, or additionally, a single value of first and / or second fuzzy set may be located at a locus 636 on first range of values 612 and / or second range 624, where a probability of membership may be taken by evaluation of first membership function 608 and / or second membership function 620 at that range point. A probability at 628 and / or a point 632 may be compared to a threshold 640 to determine whether a positive match is indicated. Threshold 640 may, in a nonlimiting example, represent a degree of match between first fuzzy set 604 and second fuzzy set 616, and / or single values therein with each other or with either set, which is sufficient for purposes of the matching process; for instance, threshold 640 may indicate a sufficient degree of overlap between an output from one or more machine learning models. Alternatively or additionally, each threshold 640 may be tuned by a machine learning and / or statistical process, for instance and without limitation as described in further detail in this 62 Attorney Docket No.1518-126PCT1 disclosure. With continued reference to FIG.6, in one or more embodiments, a degree of match between fuzzy sets may be used to classify plurality of cardiac input data 120, such as one or more ECGs, and / or plurality of cardiac indices 104a-n generated therefrom. As a nonlimiting example, if one or more cardiac indices 104a-n are associated with a fuzzy set that matches a fuzzy set of a cohort by having a degree of overlap exceeding a threshold, computing device may classify the cardiac index 104 as belonging to that cohort. Where multiple fuzzy matches are performed, degrees of match for each respective fuzzy set may be computed and aggregated through, for instance, addition, averaging, or the like, to determine an overall degree of match. With continued reference to FIG.6, in one or more embodiments, one or more cardiac indices 104a-n may be compared to multiple fuzzy sets of multiple cohorts. As a nonlimiting example, one or more cardiac indices 104a-n may be represented by a fuzzy set that is compared to each of the multiple fuzzy sets of multiple cohorts, and a degree of overlap exceeding a threshold between the fuzzy set representing the cardiac index 104 and any of the multiple fuzzy sets representing multiple cohorts may cause computing device to classify the cardiac index 104 as belonging to that cohort. As a nonlimiting example, there may be two fuzzy sets representing two cohorts, cohort A and cohort B. Cohort A may have a cohort A fuzzy set, cohort B may have a cohort B fuzzy set, and cardiac index 104 may have a cardiac index fuzzy set. Computing device may compare cardiac index fuzzy set with each of cohort A fuzzy set and cohort B fuzzy set, as described above, and classify cardiac index 104 to either, both, or neither of cohort A fuzzy set and cohort B fuzzy set. Machine learning methods as described throughout this disclosure may, in a nonlimiting example, generate coefficients used in fuzzy set equations as described above, such as without limitation ^^, ^^, and ^^ of a Gaussian set as described above, as outputs of machine learning methods. Likewise, cardiac indices 104a-n may be used indirectly to determine a fuzzy set, as cardiac index fuzzy set may be derived from outputs of one or more machine learning models that take cardiac indices 104a-n directly or indirectly as inputs. With continued reference to FIG.6, in one or more embodiments, fuzzy set comparison 600 may include a fuzzy inference model. For the purposes of this disclosure, a “fuzzy inference model” is a model that uses fuzzy logic to reach a decision and derive a meaningful outcome. As a nonlimiting example, a fuzzy inference system may be associated with degrees of diastolic dysfunction, such as “Normal,” “Grade 1”, “Grade 2”, “Grade 3”, and “Indeterminate”, as 63 Attorney Docket No.1518-126PCT1 described above. In one or more embodiments, an inferencing rule may be applied to determine a fuzzy set membership of a combined output based on the fuzzy set membership of linguistic variables. As a nonlimiting example, membership of a combined output in an “Grade 3” fuzzy set may be determined based on a percentage membership of a second linguistic variable with a first mode in a “Grade 3” fuzzy set and a percentage membership of a second linguistic variable associated with a second mode in a “Grade 2” fuzzy set. In one or more embodiments, parameters of cardiac panel 116 may then be determined by comparison to a threshold or output using another defuzzification process. Each stage of such a process may be implemented using any type of machine learning model, such as any type of neural network, as described herein. In one or more embodiments, parameters of one or more fuzzy sets may be tuned using machine learning. In one or more embodiments, fuzzy inferencing and / or machine learning may be used to synthesize outputs of plurality of cardiac models 128a-n. In some cases, outputs such as cardiac indices 104a-n may be combined to make an overall or final determination, which may be displayed with or instead of individual outputs. As another nonlimiting example, outputs may be ranked, wherein the output with the highest confidence score may be the output displayed at display device 160, or displayed first in a ranked display of result outputs. Referring now to FIG.7, an exemplary embodiment of a method 700 for tracking cardiac indices is described. With continued reference to FIG.7, at step 705, method 700 includes receiving, by processor 108, cardiac input data 120 from patient comprising plurality of cardiac signal. This step may be implemented with reference to details described above in this disclosure and without limitation. With continued reference to FIG.7, at step 710, method 700 includes inputting, by processor 108, cardiac input data 120 into cardiac panel 116, the cardiac panel 116 comprising plurality of cardiac models 128a-n, wherein at least one cardiac models 128a-n of the cardiac panel 116 is configured to calculate cardiac index 104 associated with a heart condition, the at least one cardiac models 128a-n comprises at least one trained cardiac machine learning model 132 configured to receive cardiac input data 120 as inputs and output cardiac indices 104, and the at least one cardiac models 128a-n is configured to calculate cardiac index 104 associated with diastolic dysfunction. This step may be implemented with reference to details described above in this disclosure and without limitation. 64 Attorney Docket No.1518-126PCT1 With continued reference to FIG.7, at step 715, method 700 includes generating, by processor 108, one or more cardiac indices 104a-n from cardiac panel 116 as a function of cardiac input data 120 and at least one cardiac machine learning model 132, wherein at least one cardiac index 104 of one or more cardiac indices 104a-n comprises a probability of patient satisfying at least one grading threshold 148. This step may be implemented with reference to details described above in this disclosure and without limitation. With continued reference to FIG.7, in some embodiments, method 700 may include a step of displaying, by processor 108, at least one cardiac index 104 of one or more cardiac indices 104a-n through graphical user interface (GUI). This step may be implemented with reference to details described above in this disclosure and without limitation. Referring now to FIGS.8A-B, two exemplary embodiments of GUI, 800a and 800b, for apparatus 100 are illustrated. In one or more embodiments, apparatus 100 may comprise display device 160 communicatively connected to processor 108, as described above, wherein the display device 160 is configured to display cardiac panel 116, at least one cardiac index 104 (see FIG.8A and GUI 800a), a patient profile 804, and / or time series data 140 (see FIG.8B and GUI 800b). For the purposes of this disclosure, a “display device” is a device configured to show visual information. In some cases, display device 160 may include a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display device 160 may include, but is not limited to, a smartphone, tablet, laptop, monitor, tablet, and the like. Display device 160 may include a separate device that includes a transparent screen configured to display computer-generated images and / or information. In one or more embodiments, display device 160 may be configured to visually present data through a user interface or a graphical user interface (GUI) to at least a user, wherein the user may interact with the data through the user interface or GUI, as described below. In one or more embodiments, user may view GUI through display device 160. In one or more embodiments, display device 160 may be located on remote device, as described below. With continued reference to FIGS.8A and 8B, display device 160 may include a remote device. For the purposes of this disclosure, a “remote device” is a computer device separate and distinct from apparatus 100. For example, and without limitation, remote device may include a smartphone, a tablet, a laptop, a desktop computer, or the like. In one or more embodiments, remote device may be communicatively connected to apparatus 100 such as, for example, 65 Attorney Docket No.1518-126PCT1 through network communication, through Bluetooth communication, and / or the like. In one or more embodiments, processor 108 may receive cardiac input data 120 and / or initiate one or more of subsequent steps through remote device. In one or more embodiments, one or more inputs from one or more users may be submitted through a user interface, such as a GUI, embedded within remote device, as described below. With continued reference to FIGS.8A and 8B, in one or more embodiments, apparatus 100 may further comprise a user interface. For the purposes of this disclosure, a “user interface” is a means by which a user and a computer system interact, for example, using input devices and software. User interface may include a graphical user interface (GUI), command line interface (CLI), menu-driven user interface, touch user interface, voice user interface (VUI), form-based user interface, any combination thereof, or the like. In one or more embodiments, a user may interact with user interface using computing device distinct from and communicatively connected to processor 108, such as a smartphone, tablet, or the like operated by the user. User interface may include one or more graphical locator and / or cursor facilities allowing user to interact with graphical models and / or combinations thereof, for instance using a touchscreen, touchpad, mouse, keyboard, and / or other manual data entry device. For the purposes of this disclosure, a “graphical user interface (GUI)” is a type of user interface that allows end users to interact with electronic devices through visual representations. In one or more embodiments, GUI may include icons, menus, other visual indicators or representations (graphics), audio indicators such as primary notation, display information, and related user controls. Menu may contain a list of choices and may allow users to select one from them. A menu bar may be displayed horizontally across the screen as a pull-down menu. Menu may include a context menu that appears only when user performs a specific action. Files, programs, web pages, and the like may be represented using a small picture within GUI. In one or more embodiments, GUI may include a graphical visualization of a user profile and / or the like. In one or more embodiments, processor 108 may be configured to modify and / or update GUI as a function of at least an input or the like by populating a user interface data structure and visually presenting data through modification of the GUI. With continued reference to FIGS.8A and 8B, in one or more embodiments, GUI may contain one or more interactive elements. For the purposes of this disclosure, an “interactive element” is an element within GUI that allows for communication with processor 108 by one or 66 Attorney Docket No.1518-126PCT1 more users. For example, and without limitation, interactive elements may include a plurality of tabs wherein selection of a particular tab, such as for example, by using a fingertip, may indicate to a system to perform a particular function and display the result through GUI. In one or more embodiments, interactive element may include tabs within GUI, wherein the selection of a particular tab may result in a particular function. In one or more embodiments, interactive elements may include words, phrases, illustrations, and the like to indicate a particular process that one or more users would like system to perform. A person of ordinary skill in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which user interfaces, GUIs, and / or elements thereof may be implemented and / or used as described in this disclosure. At a high level, aspects of the present disclosure are directed to apparatus and methods for detecting hypertension attributes in a patient time-series data. The apparatus includes at least a computing device comprised of a processor and a memory communicatively connected to the processor. The memory instructs the processor to receive a patient time-series data associated with a patient, wherein the patient time-series data is captured using a measurement device. The memory instructs the processor to input the patient time-series data into a hypertension panel wherein the hypertension panel comprises of a plurality of hypertension models. The memory instructs the processor to generate a hypertension attribute from the hypertension panel as a function of the patient time-series data and the hypertension model, wherein the hypertension attribute comprises generating, using a first hypertension model, a first hypertension attribute comprising a measurement of the patient yielding a first hypertension level, generating, using a second hypertension model, a second hypertension attribute comprising a measurement of the patient yielding a second hypertension level, and generating the hypertension attribute using the patient time-series data and at least one of the first hypertension model and the second hypertension model. The memory instructs the processor to generate a confidence score from the hypertension panel as a function of the patient time-series data and at least one of the first hypertension model and the second hypertension model. Referring now to FIG.9, an exemplary embodiment of apparatus 900 for detecting hypertension attributes in a patient time-series data is illustrated. Apparatus 900 may include a processor 904 communicatively connected to a memory 908. As used in this disclosure, “communicatively connected” means connected by way of a connection, attachment, or linkage between two or more relata which allows for reception and / or transmittance of information 67 Attorney Docket No.1518-126PCT1 therebetween. For example, and without limitation, this connection may be wired or wireless, direct or indirect, and between two or more components, circuits, devices, systems, and the like, which allows for reception and / or transmittance of data and / or signal(s) therebetween. Data and / or signals there between may include, without limitation, electrical, electromagnetic, magnetic, video, audio, radio and microwave data and / or signals, combinations thereof, and the like, among others. A communicative connection may be achieved, for example and without limitation, through wired or wireless electronic, digital or analog, communication, either directly or by way of one or more intervening devices or components. Further, communicative connection may include electrically coupling or connecting at least an output of one device, component, or circuit to at least an input of another device, component, or circuit. For example, and without limitation, via a bus or other facility for intercommunication between elements of a computing device. Communicative connecting may also include indirect connections via, for example and without limitation, wireless connection, radio communication, low power wide area network, optical communication, magnetic, capacitive, or optical coupling, and the like. In some instances, the terminology “communicatively coupled” may be used in place of communicatively connected in this disclosure. Further referring to FIG.9, apparatus 900 may include any “computing device” as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and / or system on a chip (SoC) as described in this disclosure. Apparatus 900 may include, be included in, and / or communicate with a mobile device such as a mobile telephone or smartphone. Apparatus 900 may include a single computing device 912 operating independently, or may include two or more computing devices 912 operating in concert, in parallel, sequentially or the like; two or more computing devices 912 may be included together in a single computing device 912 or in two or more computing devices 912. Apparatus 900 may interface or communicate with one or more additional devices as described below in further detail via a network interface device. Network interface device may be utilized for connecting processor 904 to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a 68 Attorney Docket No.1518-126PCT1 campus or other relatively small geographic space), a telephone network, a data network associated with a telephone / voice provider (e.g., a mobile communications provider data and / or voice network), a direct connection between two computing devices 912, and any combinations thereof. A network may employ a wired and / or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software etc.) may be communicated to and / or from a computer and / or a computing device 912. Processor 904 may include but is not limited to, for example, a computing device 912 or cluster of computing devices 912 in a first location and a second computing device 912 or cluster of computing devices 912 in a second location. Apparatus 900 may include one or more computing devices 912 dedicated to data storage, security, distribution of traffic for load balancing, and the like. Apparatus 900 may distribute one or more computing tasks as described below across a plurality of computing devices 912 of computing device 912, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices 912. Apparatus 900 may be implemented, as a non-limiting example, using a “shared nothing” architecture. With continued reference to FIG.9, processor 904 may be designed and / or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, processor 904 may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and / or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and / or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and / or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Processor 904 may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and / or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and / or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and / or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and / or parallel 69 Attorney Docket No.1518-126PCT1 processing. Still referring to FIG.9, processor 904 receives patient time-series data 916 associated with a patient and patient time-series data 916 is captured using measurement device 920. As used in this disclosure, a “patient time-series data” is information associated with a patient that is collection of recorded over a series of time intervals and / or using a series of temporally spaced samples. This may include, without limitation, various types of signal data, such as analog signals, digital signals, time-series signal data, spatial signals, frequency signals, multi- dimensional signals, and the like. In a non-limiting example, an analog signal is any continuous- time signal representing some other quantity, i.e., analogous to another quantity. For example, and without limitation, in an analog audio signal, the instantaneous signal voltage varies continuously with the pressure of the sound waves. Typically, analog signal refers to electrical signals; however, mechanical, pneumatic, hydraulic, and other systems may also convey or be considered analog signals. In another non-limiting example, a digital signal is a signal that represents data as a sequence of discrete values; at any given time it can only take on, at most, one of a finite number of values. In some cases, digital signals may represent information in discrete bands of analog levels, wherein all levels within a band of values represent the same information state. In a non-limiting example, a digital signal may be represented as a digital circuit. Typically, digital circuit signals can have two possible valid values; a binary signal or logic signal wherein the binary signal and the logic signal are represented by two voltage bands: one voltage band that is near a reference value, and the other voltage value that is near the supply voltage. The voltage bands correspond to the two values "zero" and "one" (or "false" and "true") of the Boolean domain, wherein at any given time, a binary signal represents one binary digit (bit). Without limitation, digital signals are generally used for communications and processing within electronic devices and computer systems. In another non-limiting example, time-series signal data is information in the form of a signal that is collected and recorded over consistent intervals of time. Without limitation, time-series signal data may be used in order to extract meaningful statistics and other characteristics of the data. Time-series signal data can be classified into two main types: continuous-time series signals and discrete-time signals. Continuous-time signals are signals that are measured and recorded over a continuous range, including, but not limited to, analog signals, such as sound waves and temperature measurements (from analog devices like analog thermometers). On the other hand, discrete-time signals are 70 Attorney Docket No.1518-126PCT1 recorded at specific, distinct points. For example, and without limitation, discrete-time signals may include digital sensor measurements and financial market data sampled at fixed intervals. In another non-limiting example, patient time-series data 916 may include an electrocardiogram signal wherein the electrocardiogram signal may include an electrocardiogram datum. As used herein, an “electrocardiogram datum” is a single data point obtained from the electrical activity of the heart of a patient. An electrocardiogram datum may be derived from an electrocardiogram signal. In some embodiments, an electrocardiogram datum may include a rhythm strip electrocardiogram datum. As used herein, a “rhythm strip electrocardiogram datum” is a datum describing electrical activity detected using a single electrode. In some embodiments, an electrocardiogram datum may include a median beat electrocardiogram datum. As used herein, a “median beat electrocardiogram datum” is a datum describing electrical activity detected using a plurality of leads and / or electrodes. In some embodiments, an electrocardiogram datum may include data collected by 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or more electrocardiogram leads. For example, an electrocardiogram datum may include a median beat collected by 12 electrocardiogram leads. A “lead,” as used in this disclosure, is one or more electrodes attached to the skin to detect a heart’s electric signals. As used in this disclosure, a “standard 12-lead electrocardiogram signal” is a measurement the electrical activity of a heart from 12 different perspectives. In a non-limiting embodiment a standard 12-lead electrocardiogram signal may include a graphical record of the direction and magnitude of the electrical activity generated by the depolarization and repolarization of the atria and ventricles of the heart. With continued reference to FIG.9, patient time-series data 916 may include temporal data, and metadata. As used in this disclosure, a “temporal data” is information which is collected and / or recorded over a continuous-time interval or discrete-time interval. Temporal data captures signal data changes over time and provides time-stamped data recordation. As used in the current disclosure, “metadata” refers to descriptive or informational data that provides details about the digital electrocardiogram data. Metadata may include descriptive metadata, wherein descriptive metadata is configured to describe the content, context, and structure of the data. In an embodiment, metadata may include data regarding the lead system the digital electrocardiogram data was recording. Electrocardiograms are typically recorded using multiple leads, each of which provides a different view of the heart's electrical activity. Common lead systems include the 12-lead, 6-lead, 3-lead, and single-lead electrocardiograms. The specific lead 71 Attorney Docket No.1518-126PCT1 system used to generate the digital electrocardiogram data and their configurations may be documented in the metadata. In some embodiments, metadata associated with the digital electrocardiogram data may include information such as time, geographic location, medical facility names, medical professional logs, patient names, patient IDs, patient data, along with any other patient specific data. Metadata may be used to describe records of how the data has been accessed, utilized, or modified over time, aiding in understanding data usage patterns, and optimizing access. With continued reference to FIG.9, patient time-series data 916 may include electrocardiogram signals. As used in the current disclosure, a “electrocardiogram signal” is a signal representative of electrical activity of heart. The electrocardiogram signal may consist of several distinct waves and intervals, each representing a different phase of the cardiac cycle. These waves may include the P-wave, QRS complex, T wave, U wave, and the like. The P-wave may represent atrial depolarization (contraction) as the electrical impulse spreads through the atria. The QRS complex may represent ventricular depolarization (contraction) as the electrical impulse spreads through the ventricles. The QRS complex may include three waves: Q wave, R wave, and S wave. The T-wave may represent ventricular repolarization (recovery) as the ventricles prepare for the next contraction. The U-wave may sometimes be present after the T wave, it represents repolarization of the Purkinje fibers. The intervals between these waves provide information about the duration and regularity of various phases of the cardiac cycle. The electrocardiogram signal can help diagnose various heart conditions, such as arrhythmias, myocardial infarction (heart attack), conduction abnormalities, and electrolyte imbalances. In an embodiment, patient time-series data 916 is captured using measurement device 920. With continued reference to FIG.9, as used in this disclosure, a “measurement device” is any device that is able to extract any kind of data from a patient. In a non-limiting embodiment, measurement device 920 may include a transducer. As used in this disclosure, a “transducer” is a device used to transform one kind of energy into another. When a transducer converts a quantity of energy to an electrical voltage or an electrical current it is called a sensor. A measurable quantity of energy may include sound pressure, optical intensity, magnetic field intensity, thermal pressure, etc. When a transducer converts an electrical signal into another form of energy such as sound, light, mechanical movement, it is called an actuator. It should be noted that sound is incidentally a pressure field. Actuators allow the use of feedback at the source of the 72 Attorney Docket No.1518-126PCT1 measurements. In a non-limiting embodiment, a transducer may detect at least a cardiac phenomenon and output patient time-series data 916. In another non-limiting example, a transducer may include a plurality of clinical transducers. As used in this disclosure, a “plurality of clinical transducers” is a transducer device used in the medical field to measure, analyze, and / or quantify electrical signals in a body. Plurality of clinical transducers may generate training data for apparatus 900 and training data may be stored in an electronic health record database as described in more detail below. In a non-limiting embodiment, training data may include de-identified health records. With continued reference to FIG.9, measurement device 920 may be considered as a component or with a collection of electronics such as amplifiers, decoders, filters, computer devices a transducer. For the purposes of this disclosure an “instrument” is a sensor bundled with its associated electronics. However, in some embodiments, sensors may be further integrated with a transducer. Sensors may be integrated with wearable electrocardiogram devices such as, without limitation, electrocardiogram monitoring watches, bio stickers, portable electrocardiogram measuring devices, and the like. With continued reference to FIG.9, measurement device 920 integrated with a transducermay be linear so that response y to a stimulus x is in the form: ^^^^^^ ൌ ^^^^, 0 ^ ^^ ^ ^^^^௫, ^^ ^ 0.It should be noted, there is a presumption that the stimulus to be positive. A is the sensitivity of the transducer gain, or the gain of the sensor. The gain is presumed to be positive for which thelinear model satisfies the definition of linearity: ^^^^^ ^ ^^^ ൌ ^^^^^ ^ ^^^ ൌ ^^^^^^ ^ ^^^^^^. It shouldbe noted that this example is an idealized form of measurement device 920 and may extend beyond the linearity constraints which may include time dependency, memory, and its output keeping track of input. A more generalized sensor may include the steady state transfer function of the sensor. For this case, the sensitivity can be defined as the derivative of the output withrespect to the input: ^^ ൌ డ௬In this example, the sensor exhibits sensitivities to other operating parameters (i.e., supply or temperature. For the purposes of this disclosure, “sensitivity” is the ratio of output to input. This can include electrical output and signal input or an input transducer. It can also include physical output to an electrical input, or an output transducer. Sensitivity can also be used in its usual electrical meaning. In this it would refer to a percent change of a property of a device because of a percent change in a parameter. In some embodiments this would be a percent change in gain as a result of percent change in ambient 73 Attorney Docket No.1518-126PCT1 temperature. This type of sensitivity may be referred to as the Gain of a sensor. Still referring to FIG.9, a transducer with integrated sensors may not respond to arbitrarily small signals. A transducer may respond to signals within a specified range from zero to a sensor threshold which does not cause the output of the sensor to change. The existence of a threshold relates to the nonlinear behavior of the device and the noise. A transducer with an integrated sensor may fail to respond to stimuli which are arbitrarily large as well. In this case, a transducer integrated with measurement device 920 may have a max range. The full range of a transducer integrated with measurement device 920 may be limited by compression or clipping. Compression and clipping are results of nonlinearity and thus may include a transducer as a nonlinearity device. Still referring to FIG.9, referring to the linear equation above assuming a linear sensor isimproved with the addition of a constant: ^^^^^^ ൌ ^^^ ^ ^^^^. It should be noted that the equation isnot linear even though it is described as a first order polynomial. The constant is called a zero offset and can be defined in two ways: a sensor reading when the input is zero, or the value of the stimulus required to make the output zero. The zero offset is corrected by subtracting ^^^fromy and recovering the linear description of a sensor: ^^ᇱ^^^^ ൌ ^^^^^^ െ ^^^ ൌ ^^^^.With continued reference to FIG.9, a transducer may include very fast measurements where it can internally store energy. A transducer output may depend on previous measurements the integrated sensors make. It should be noted that the sensor may exhibit memory. The time dependence of measurement device 920 can be linear if the response is described by a lineardifferential equation: ∑ே ^ୀ^ ^^ డ^௬^ ൌ ∑^ ^ୀ^^^ డೖ௫^. Taking the Laplace transform of this equation: ^ೖ^^^^^,^^^ ൌ ൬∑ೖసబ^ೖௌ ^Laplace transform space and the sensor in stimulus x. The response of measurement device 920 with a transfer function H(s) at time t is the convolution integral between the history of the stimulus x and theinverse Laplace transform h(t) of H(s): ^^^^^^ ൌ ^^ ^ℎ^^^^^^^^^ െ ^^^^^^^.A transducer may behave like a low pass filter, wherein there is a to their input. There is a limit to the maximum stimulus frequency that can be detected. The maximum frequency measurement device 920 can interpret is approximately the inverse of its response time. In a nonlimiting example, measurement device 920 may include imaging devices, ultrasound device, echocardiogram devices, electrocardiogram devices, electroencephalogram devices, and the like. 74 Attorney Docket No.1518-126PCT1 With continued reference to FIG.9, patient time-series data 916 may include a matrix. In an embodiment, the matrix may be represented by vectors. As used in this disclosure, a “vector” is a data structure that represents one or more quantitative values and / or measures the position vector. Such vector and / or embedding may include and / or represent an element of a vector space; a vector may alternatively or additionally be represented as an element of a vector space, defined as a set of mathematical objects that can be added together under an operation of addition following properties of associativity, commutativity, existence of an identity element, and existence of an inverse element for each vector, and can be multiplied by scalar values under an operation of scalar multiplication compatible with field multiplication, and that has an identity element is distributive with respect to vector addition, and is distributive with respect to field addition. A vector may be represented as an n-tuple of values, where n is one or more values, as described in further detail below; a vector may alternatively or additionally be represented as an element of a vector space, defined as a set of mathematical objects that can be added together under an operation of addition following properties of associativity, commutativity, existence of an identity element, and existence of an inverse element for each vector, and can be multiplied by scalar values under an operation of scalar multiplication compatible with field multiplication, and that has an identity element is distributive with respect to vector addition, and is distributive with respect to field addition. Each value of n-tuple of values may represent a measurement or other quantitative value associated with a given category of data, or attribute, examples of which are provided in further detail below; a vector may be represented, without limitation, in n- dimensional space using an axis per category of value represented in n-tuple of values, such that a vector has a geometric direction characterizing the relative quantities of attributes in the n-tuple as compared to each other. Two vectors may be considered equivalent where their directions, and / or the relative quantities of values within each vector as compared to each other, are the same; thus, as a non-limiting example, a vector represented as [5, 10, 15] may be treated as equivalent, for purposes of this disclosure, as a vector represented as [1, 2, 3]. Vectors may be more similar where their directions are more similar, and more different where their directions are more divergent, for instance as measured using cosine similarity as computed using a dot product of two vectors; however, vector similarity may alternatively or additionally be determined using averages of similarities between like attributes, or any other measure of similarity suitable for any n-tuple of values, or aggregation of numerical similarity measures for 75 Attorney Docket No.1518-126PCT1 the purposes of loss functions as described in further detail below. Any vectors as described herein may be scaled, such that each vector represents each attribute along an equivalent scale of values. Each vector may be “normalized,” or divided by a “length” attribute, such as a lengthattribute l as derived using a Pythagorean norm: ^^ ൌ ^∑^ ଶ^ୀ^ ^^^ , where ai is attribute number i ofthe vector. Scaling and / or normalization may function to make vector comparison independent of absolute quantities of attributes, while preserving any dependency on similarity of attributes. A two-dimensional subspace of a vector space may be defined by any two orthogonal vectors contained within the vector space. Two-dimensional subspace of a vector space may be defined by any two orthogonal and / or linearly independent vectors contained within the vector space; similarly, an n-dimensional space may be defined by n vectors that are linearly independent and / or orthogonal contained within a vector space. A vector’s “norm' is a scalar value, denoted‖^^‖indicating the vector's length or size, and may be defined, as a non-limiting example, according to a Euclidean norm for an n-dimensional vector a as: ^ ^ In an embodiment, and with to FIG.9, hypertension attributes 932a-c of patient time-series data 916 may be represented by a dimension of a vector space; as a non- limiting example, each element of a vector may include a number representing an enumeration of co-occurrences of first hypertension attribute 932a represented by the vector with second hypertension attribute 932b. Hypertension attribute may include hypertension. Hypertension attribute may include hypertensive heart disease. As used in this disclosure, a “hypertension attribute” is a characteristic of a signal that is derived from the electroactivity of a patient’s heart and related to the patient’s blood pressure levels. Hypertension attributes 932a-c may include information related to a frequency domain feature. A frequency domain feature may include information about the frequency distributions that are present in an ECG signal. Hypertension attributes 932a-c may describe either a static signal or a particular time period of a dynamic signal. Hypertension attributes 932a-c may be obtained from a signal by analyzing the composition of the signal frequencies to identify unique patterns and / or irregularities in the signal. Without limitation, hypertension attributes 932a-c may include power spectral density, and the like. Alternatively, or additionally, dimensions of vector space may not represent distinct 76 Attorney Docket No.1518-126PCT1 hypertension attributes 932a-c, in which case elements of a vector representing a first hypertension attribute 932a may have numerical values that together represent a geometrical relationship to a vector representing a second hypertension attribute 932b, wherein the geometrical relationship represents and / or approximates a semantic relationship between the first hypertension attribute 932a and the second hypertension attribute 932b. Vectors may be more similar where their directions are more similar, and more different where their directions are more divergent; however, vector similarity may alternatively or additionally be determined using averages of similarities between like attributes, or any other measure of similarity suitable for any n-tuple of values, or aggregation of numerical similarity measures for the purposes of loss functions as described in further detail below. Any vectors as described herein may be scaled, such that each vector represents each attribute along an equivalent scale of values. In an embodiment associating hypertension attributes 932a-c to one another as described above may include computing a degree of vector similarity between a vector representing hypertension attributes 932a-c and a vector representing another hypertension attributes 932a-c; vector similarity may be measured according to any norm for proximity and / or similarity of two vectors, including without limitation cosine similarity. As used in this disclosure “cosine similarity” is a measure of similarity between two-non-zero vectors of a vector space, wherein determining the similarity includes determining the cosine of the angle between the two vectors. Cosine similarity may be computed as a function of using a dot product of the two vectors divided by the lengths of the two vectors, or the dot product of two normalized vectors. For instance, and without limitation, a cosine of 0º is 1, wherein it is less than 1 for any angle in the interval (0,π) radians. Cosine similarity may be a judgment of orientation and not magnitude, wherein two vectors with the same orientation have a cosine similarity of 1, two vectors oriented at 90° relative to each other have a similarity of 0, and two vectors diametrically opposed have a similarity of -1, independent of their magnitude. As a non- limiting example, vectors may be considered similar if parallel to one another. As a further non- limiting example, vectors may be considered dissimilar if orthogonal to one another. As a further non-limiting example, vectors may be considered uncorrelated if opposite to one another. Additionally, or alternatively, degree of similarity may include any other geometric measure of distance between vectors. Patient time-series data 916 may use a matrix to represent variables and / or parameters of 77 Attorney Docket No.1518-126PCT1 the statistical model. As used in this disclosure “matrix” is a rectangular array or table of numbers, symbols, expressions, vectors, and / or representations arranged in rows and columns. For instance, and without limitation, matrix may include rows and / or columns comprised of vectors representing hypertension attributes 932a-c, where each row and / or column is a vector representing a distinct hypertension attribute 932; hypertension attributes 932a-c represented by vectors in matrix may include all frequency bands over a range of frequencies as described above as the statistical model identifies the hypertension attribute 932, including without limitation the magnitude and phase of a set of sinusoids at the frequency components of the signal as described above. As a non-limiting example matrix may include how a signal is distributed within different frequency bands over a range of frequencies. A matrix may be generated by performing a singular value decomposition function. As used in this disclosure a “singular value decomposition function” is a factorization of a real and / or complex matrix that generalizes the eigen decomposition of a square normal matrix to any matrix of m rows and n columns via an extension of the polar decomposition. For example, and without limitation singular value decomposition function may decompose a first matrix, A, comprised of m rows and n columns to three other matrices, U, S, T, wherein matrix U, represents left singular vectors consisting of an orthogonal matrix of m rows and m columns, matrix S represents a singular value diagonal matrix of m rows and n columns, and matrix VT represents right singular vectors consisting of an orthogonal matrix of n rows and n columns according to the vectors consisting of an orthogonal matrix of n rows and n columns according to the function: ^^ ൌ ^^ ^ ்^௫^ ^௫^ ^^௫^^^^௫^singular value decomposition function may find eigenvalues and eigenvectors of AATand ATA. The eigenvectors of ATA may include the columns of VT, wherein the eigenvectors of AATmay include the columns of U. The singular values in S may be determined as a function of the square roots of eigenvalues AATor ATA, wherein the singular values are the diagonal entries of the S matrix and are arranged in descending order. Singular value decomposition may be performed such that a generalized inverse of a non-full rank matrix may be generated. Still referring to FIG.9, processor 904 inputs patient time-series data 916 into hypertension panel 924, wherein hypertension panel 924 includes a plurality of hypertension models. As used in this disclosure, a “hypertension panel” is a set of algorithms or machine 78 Attorney Docket No.1518-126PCT1 learning models that are used to evaluate and monitor a patient's blood pressure. For example, and without limitation, hypertension panel 924 may include a set of algorithms configured to receive electrocardiogram signals associated with a patient. In one or more embodiments, hypertension panel 924 may be configured to provide a comprehensive view of a patient's journey through a heart condition. For example, and without limitation, hypertension panel 924 may include algorithms configured to receive electrocardiogram signals and output information associated with heart conditions such as but not limited to elevated blood pressures, hypertension stage 1 and stage 2, uncontrolled hypertension, and the like. In one or more embodiments, algorithms may include machine learning models, linear regression models and / or any other mathematical models configured to receive a variable such as patient time-series data 916 (e.g., electrocardiogram input data) and output a medical condition and / or information that may be indicative of a medical condition. In one or more embodiments, hypertension panel 924 may contain a distinct set of algorithms configured to monitor and / or generate a patient’s health in association with a particular disease. For example, and without limitation, a first hypertension panel may be used to monitor hypertension in a patient wherein the first hypertension panel may output the percentage of blood leaving the heart whereas a second hypertension panel may be used to monitor a degree of heart arrythmia associated with the patient. In one or more embodiments, hypertension panel 924 may be used to monitor and / or determine a particular heart disease associated with patient such as but not limited to, arrythmias, tachycardia, bradycardia, heart attacks, coronary heart disease and the like. In one or more embodiments, hypertension panel 924 may be configured to receive the same or similar inputs and output differing results. In one or more embodiments, hypertension panel 924 may receive patient time-series data 916, and output a differing heart disease or heart condition. With continued reference to FIG.9, as used in this disclosure, a “hypertension model” is an individual system, algorithm module, and / or machine learning model used to evaluate and monitor a patient’s blood pressure. Without limitation, hypertension models 928a-c may be trained using data from a plurality of clinical transducers, electronic health records, and the like. In one or more embodiment, hypertension models 928a-c may be trained on the training data in any manner described herein. In a non-limiting example, hypertension models 928a-c may include a statistical model, wherein the statistical model determines whether specific hypertension attributes 932a-c are present in patient time-series data 916. As used in this 79 Attorney Docket No.1518-126PCT1 disclosure, a “statistical model” is a mathematical representation of relationships observed in data. A statistical model may provide insight on data regarding specific patterns and the like. A statistical model may describe, analyze, and make predictions based on the input data. With continued reference to FIG.9, hypertension models 928a-c may include a fuzzy set comparison model as described in more detail in FIG.6. In a no-limiting example, the fuzzy set comparison model may include a fuzzy inference model. As used in this disclosure, a “fuzzy inference model” uses fuzzy logic to reach a decision and derive a meaningful outcome. In a non-limiting example, fuzzy inference system may be associated with degrees of hypertension levels, such as, “elevated blood pressure,” “stage 1 hypertension,” “stage 2 hypertension,” and “critical hypertension.” In some embodiments, an inferencing rule may be applied to determine fuzzy set membership of a combined output based on fuzzy set membership of linguistic variables. In a non-limiting example, membership of a combined output in an “elevated blood pressure” fuzzy set may be determined based on a percentage membership of a second linguistic variable with a first mode in an “elevated blood pressure” fuzzy set and a percentage membership of a second linguistic variable associated with a second mode in a “stage 1 hypertension” fuzzy set. In some embodiments, hypertension panel parameters may then be determined by comparison to a threshold or output using another defuzzification process. Each stage of such a process may be implemented using any type of machine learning model such as any type of neural network described herein. In some embodiments parameters of one or more fuzzy sets may be tuned using machine learning. In one or more embodiment, fuzzy inferencing and / or machine learning may be used to synthesize the outputs of a plurality of hypertension models, for example, in some cases, the output (e.g., hypertension attribute 932a-c), may be combined to make an overall or final determination, which may be displayed with or instead of individual outputs. In another non-limiting example, the outputs may compete, for example, the output with the highest confidence score 940 may be the output displayed at remote device 948, or displayed first in a ranked display of result outputs. Alternatively or additionally, hypertension models 928a-c may include a machine learning model as described in more detail in FIG.3. Alternatively or additionally, the machine learning model as described in FIG.3 may include a neural network as detailed in FIG.4 and FIG.5. The machine learning model may also include a convolutional neural network. In an embodiment, specific signal characteristics may be extracted by utilizing deep neural networks, 80 Attorney Docket No.1518-126PCT1 which may learn and identify complex patterns and features in the ECG signals that may not be immediately apparent through traditional analysis methods. In some embodiments, the machine learning model may include a classifier. With continued reference to FIG.9, hypertension attribute may include information associated with the function of a patient’s heart. For example, and without limitation, hypertension attributes 932a-c may include information associated with high blood pressure, such as 120 / 80 mm Hg. In one or more embodiments, hypertension attributes 932a-c may be used to determine a level or value associated with a particular heart condition. For example, and without limitation, hypertension attributes 932a-c may include a hypertension level. In another non limiting example, hypertension attributes 932a-c may be used to determine how well a heart is functioning as a numerical value. In one or more embodiments, hypertension attributes 932a-c may include a level of probability associated with a heart condition, wherein the level of probability may indicate the probability that the patient has the condition currently, and / or the probability the patient will have the condition in the future. In one or more embodiments, hypertension attributes 932a-c may include, but is not limited to, characteristics used to indicate a level of atrial fibrillation, tachycardia, premature beats, bradycardia, heart block, heart palpitations and the like. In one or more embodiments, hypertension panel 924 may be configured to output differing hypertension attributes 932a-c. In one or more embodiments, hypertension attributes 932a-c may include the severity of the heart disease or heart condition. In one or more embodiments, the severity may be rated on a scale of 0 to 900 wherein a 0 may indicate that that there is no severity and 900 may indicate that the heart condition is quite severe. In one or more embodiments, hypertension attributes 932a-c may include numerical amounts associated with each condition wherein the numerical amount may indicate the severity of the condition and / or numerical amounts that can be compared to reference ranges in order to determine the severity of the heart condition. In one or more embodiments, hypertension attributes 932a-c may include vector loops indicating electrical signals within the heart, wherein vector loops may be used to determine the severity of the condition. In one or more embodiments, cardiac values may include P waves and QRS complexes which may be used to determine the severity of a condition. With continued reference to FIG.9, hypertension attributes 932a-c may include a hypertension level of the patient wherein the hypertension level may include a measurement of 81 Attorney Docket No.1518-126PCT1 systolic and / or diastolic blood pressure levels. In one or more embodiments, high blood pressure levels may indicate the hearts ability to regulate blood pressure. In one or more embodiments, a blood pressure level of 120 / 80 mm Hg may indicate that the patient has normal blood pressure. In one or more embodiments, a blood pressure level with systolic readings between 920-129 and diastolic readings between 80-89 mm Hg may indicate that the patient has an elevated blood pressure. In one or more embodiments, a blood pressure level with systolic readings between 930-139 and diastolic readings between 80-89 mm Hg may indicate that the patient has stage 1 hypertension. In one or more embodiments, a blood pressure level with systolic reading of 940 and diastolic reading of 90 mm Hg or higher may indicate that the patient has stage 2 hypertension. In one or more embodiments, a blood pressure level of 980 / 120 mm Hg may indicate the patient is experiencing a hypertensive crisis, requiring immediate medical attention. In one or more embodiments, a determined hypertension level can be used to determine if emergency medical assistance is required prior to heart failure occurring. In one or more embodiments, hypertension attributes 932a-c may include a blood pressure level of the patient. In one or more embodiments, hypertension attributes 932a-c may include changes in the blood pressure level. With continued reference to FIG.9, in one or more embodiments, shape of an ECG waveform may be influenced by a number of factors. For example and without limitation, subtle deformations may be imparted on one or more portions of the ECG waveform for a patient who has high blood pressure as compared to another patient who has normal blood pressure levels. The underlying disease affecting the heart, whether due to atherosclerosis, myopathic processes, inflammation, valvular derangements from any cause, can impair the heart muscle's pumping capability. The underlying disease may similarly affect the metabolism of individual myocytes or their interconnections, and lead to deposition of fibrosis or infiltration of inflammatory cells, all of which lead to subtle electrical changes. These local cardiac electrical changes may contribute to deformations recorded on the surface ECG and / or within signal data 916. Such deformations may not be visible with the naked eye, but may nonetheless be detectable using computer-based models according to the techniques disclosed herein. With continued reference to FIG.9, hypertension panel 924 is configured to receive patient time-series data 916 as an input, calculate hypertension attributes 932a-c, and output hypertension attributes 932a-c. In a nonlimiting example, hypertension panel 924 may receive a 82 Attorney Docket No.1518-126PCT1 patient time-series data 916, such as, without limitation, a patient’s ECG data, and hypertension panel 924 may calculate hypertension attributes 932a-c based on the patient's ECG data, and output the attribute to a display device for the patient to understand their risk for hypertension and / or their current blood pressure and what that measurement reading means. With continued reference to FIG.9, plurality of hypertension models may include a hypertension classifier model, a hypertension prediction model, and a hypertension correlation model, as described in more detail below. With continued reference to FIG.9, plurality of hypertension models may include a loss function. In one or more embodiments, hypertension models 928a-c may utilize a loss function in order to measure the discrepancy between predicted outputs of hypertension models 928a-c and the actual blood pressure reading in the patient. In one or more embodiments, hypertension models 928a-c may adjust parameters iteratively through optimization techniques such as, but not limited to gradient descent to minimize the discrepancy. In one or more embodiments, a machine learning model, such as hypertension models 928a-c, may include parameter values. “Parameter values” for the purposes of this disclosure are internal variables that a machine learning model has generated from training data in order to make predictions. In one or more embodiments, parameter values may be adjusted during training or pretraining in order to minimize a loss function. In one or more embodiments, during training, predicted outputs of hypertension models 928a-c are compared to actual outputs wherein the discrepancy between predicted output and actual outputs are measured in order to minimize a loss function. A loss function also known an “error function” may measure the difference between predicted outputs and actual outputs in order to improve the performance of the machine learning model. A loss function may quantify the error margin between a predicted output and an actual output wherein the error margin may be sought to be minimized during the training process. The loss function may allow for minimization of discrepancies between predicted outputs and actual outputs of the machine learning model. In one or more embodiments, the loss function may adjust parameter values of the machine learning model. In one or more embodiments, in a linear regression model, parameter values may include coefficient assigned to each feature and the bias term. In one or more embodiments, in a neural network, parameter values may include weights and biases associated with the connection between neurons or nodes within layers of the network. In one or more embodiments, processor 83 Attorney Docket No.1518-126PCT1 904 may be configured to minimize a loss function by adjusting parameter values of hypertension models 928a-c based on discrepancies between predicted outputs and actual outputs. In one or more embodiments, processor 904 may be configured to iteratively pretrain hypertension models 928a-c, wherein processor 904 may be configured to iteratively receive patient time-series data 916 from patients and adjust parameter values of hypertension models 928a-c. In an embodiment, the more patient time-series data 916 received by hypertension models 928a-c, the more accurate the hypertension models 928a-c may be in predicting hypertension levels in the patient. In one or more embodiments, parameter values may correspond to learned features of patient time-series data 916, such as, without limitation, waveforms, patterns, frequencies and the like. With continued reference to FIG.9, apparatus 900 may include training hypertension models 928a-c which may include receiving a plurality of patient time-series data examples associated with a plurality of patients, pretraining hypertension models 928a-c in hypertension panel 924 as a function of plurality of patient time-series data examples by adjusting one or more parameter attributes of hypertension models 928a-c, and training hypertension models 928a-c as a function of the parameter attributes and electronic health records. As used in this disclosure, “electronic health records” refer to digital information related to a patient’s medical history. Electronic health records may include de-identified information related a patient’s medical diagnoses, medications, treatments, lab results, and any other related information. In an embodiment, electronic health records may include electrocardiogram data from a patient, wherein the electrocardiogram data may include a plurality of signals. With continued reference to FIG.9, hypertension attributes 932a-c may include a hypertension deviation 936, wherein the hypertension deviation 936 includes a change in hypertension attributes 932a-c. As used in this disclosure, a “hypertension deviation” is a change in hypertension attributes 932a-c. For example and without limitation, hypertension deviation 936 may indicate that a patients risk of heart attack has increased 15% (e.g., from 20% to 35%). In another non limiting example, hypertension deviation 936 may indicate a change in blood pressure wherein hypertension deviation 936 may indicate that the blood pressure of the patient has increased from 120 / 80 mm Hg to 130 / 80 mm Hg. In one or more embodiments, blood pressure levels may be indicated by the patient’s blood pressure levels which is recorded using the systolic blood pressure and the diastolic blood pressure. The systolic blood pressure indicates 84 Attorney Docket No.1518-126PCT1 how much pressure a patient’s blood is exerting against their artery walls when the heart contracts and is represented by the first number of the blood pressure reading. The diastolic blood pressure indicates how much pressure the patient’s blood is exerting against their artery walls while the heart muscles are resting between contractions and is represented by the second number of a blood pressure reading. For example, stage 2 hypertension may be recorded as 140 / 90 mm Hg or higher indicating that the heart is exerting an elevated pressure of 940 mm Hg of pressure against the patients artery walls when the heart contracts and 90 mm Hg of pressure against the patients artery walls when the heart is resting. In one or more embodiments, hypertension panel 924 may be configured to output blood pressure levels of the patient. In one or more embodiments, blood pressure levels may be determined by analyzing changes in waveforms associated with signals. In one or more embodiments, increased QRS complex, increased R wave amplitude, and increased S wave depth in specific leads may indicate left ventricular hypertrophy (LVH) which may be associated with a particular stage of hypertension. In one or more embodiments, hypertension panel 924 may receive patient time-series data 916 and determine a stage of hypertension in the patient. In one or more embodiments, hypertension deviation 936 may indicate a change in blood pressure levels, such as for example, an increase from 120 / 80 mm Hg to 130 / 80 mm Hg. In one or more embodiments, hypertension deviation 936 may be generated as a function of hypertension panel 924. In one or more embodiments, processor 904 may receive hypertension attributes 932a-c from a previous iteration and compare the previous hypertension attributes 932a-c to hypertension attribute of a current iteration. In one or more embodiments, the change between the output of the previous iteration and the output of the current iteration may be used as hypertension deviation 936. In one or more embodiments, hypertension deviation 936 may be generated as a function of a comparison between previous patient time-series data 916 received and patient time-series data 916 received from the current iteration wherein changes between signals may indicate hypertension deviation 936. In one or more embodiments, hypertension deviation 936 may be visually displayed to a patient, such as through a graph depicting changes in hypertension attributes over time. In one or more embodiments, cardiac deviations may be used to indicate to a patient how their health has increased or decreased since a previous appointment, visit, generation of hypertension attributes 932a-c and the like. In one or more embodiments, at least one hypertension attribute may include hypertension deviation 936 wherein hypertension deviation 936 includes a change in 85 Attorney Docket No.1518-126PCT1 hypertension attribute. With continued reference to FIG.9, hypertension deviation 936 may be used to predict future hypertension attributes and / or determine trends for hypertension attributes. In one or more embodiments, hypertension deviation 936 may be used to determine if a patient requires medication prior to experiencing a heart failure. In one or more embodiments, hypertension deviation 936 may be used to prevent various heart conditions before they occur. In one or more embodiments, hypertension deviation 936 may be used to determine if a particular medication or treatment is working due to changes in hypertension attributes. Still referring to FIG.9, processor 904 generates hypertension attributes 932a-c from hypertension panel 924 as a function of patient time-series data 916 and hypertension model 928a-c, wherein hypertension attributes 932a-c includes generating, using a first hypertension model, a first hypertension attribute which includes a measurement of the patient yielding a first hypertension level and generating, using a second hypertension model, a second hypertension attribute which includes a measurement of the patient yielding a second hypertension level, and generating hypertension attribute 932a-c using patient time-series data 916 and at least one of the first hypertension model and the second hypertension model. As used in this disclosure, a “hypertension level” is a range of blood pressure levels that are associated with particular hypertension categories. For example and without limitation, hypertension level may include a range of blood pressure levels ranging from 920-180 in the systolic measurement and around 50- 90 in diastolic measurement. In one or more embodiments, a patient may yield a hypertension level by having a blood pressure within a range of the hypertension level. For example, and without limitation, a patient may yield a hypertension level of between 120 / 80 mm Hg and 925 / 85 mm Hg in instances in which the patient has a hypertension level of 922 / 84 mm Hg. In one or more embodiments, falling within a particular hypertension level may indicate an underlying heart disease. In one or more embodiments, falling within a particular hypertension level may indicate an underlying heart disease. In one or more embodiments, falling within a particular hypertension level may indicate an underlying heart disease. For example, and without limitation, a patient yielding a hypertension level of 120 / 80 mm Hg may indicate that the patient has a normal blood pressure level. For example, and without limitation, a patient yielding a hypertension level of between 120 / 80 mm Hg and 929 / 50 mm Hg may indicate that the patient has an elevated blood pressure. For example, and without limitation, a patient yielding a 86 Attorney Docket No.1518-126PCT1 hypertension level of between 130 / 80 mm Hg and 939 / 89 mm Hg may indicate that the patient has hypertension stage 1. For example, and without limitation, a patient yielding a hypertension level of between 140 / 90 mm Hg and higher may indicate that the patient has hypertension stage 2. For example, and without limitation, a patient yielding a hypertension level over 980 / 120 mm Hg may indicate that the patient is experiencing a hypertensive crisis and requires medical attention immediately. In one or more embodiments, hypertension panel 924 may be configured to calculate hypertension attributes 932a-c for each hypertension level. For example, and without limitation, a first hypertension panel may be configured to determine a first hypertension attribute associated with a first hypertension level and a second hypertension panel may be used to determine a second hypertension attribute associated with a second hypertension level. In an embodiment, hypertension panel 924 may be configured to output a given value that the patient falls within a range of hypertension levels. Still referring to FIG.9, processor 904 generates confidence score 940 from hypertension panel 924 as a function of patient time-series data116 and hypertension models 928a-c. As used herein, a “confidence score” is a degree of confidence that hypertension attributes 932a-c are accurate. In some embodiments, confidence score 940 may be determined as a function of a machine learning model, such as hypertension models 928a-c. Confidence score 940 may be used to predict how likely hypertension models 928a-c output is to be accurate. For example, in some classifiers, numerical values are calculated, and a cutoff value is used to determine which category the input fits into. In this example, the numerical value may be used to determine a certainty score based on how closely it fits into a class and / or how close to a decision boundary it is. In another example, in clustering algorithms, certainty scores may be calculated based on how closely an input fits into a cluster. In some embodiments, hypertension attributes 932a-c are generated without the use of hypertension models 928a-c, and confidence score 940 is generated using other methods. Both hypertension attributes 932a-c and confidence score 940 may be displayed through display device 944 as described further below. Still referring to FIG.9, processor 904 may display, using display device 944 of remote device 948, hypertension attributes 932a-c through a graphical user interface. As used in this disclosure, a "display device" refers to an electronic device that visually presents information to an entity. In some cases, display device 944 may be configured to project or show visual content generated by computers, video devices, or other electronic mechanisms. In some cases, display 87 Attorney Docket No.1518-126PCT1 device 944 may include a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. In a non-limiting example, one or more display devices may vary in size, resolution, technology, and functionality. Display device 944 may be able to show any data elements and / or visual elements as listed above in various formats such as, textural, graphical, video among others, in either monochrome or color. Display device 944 may include, but is not limited to, a smartphone, tablet, laptop, monitor, tablet, and the like. Display device 944 may include a separate device that includes a transparent screen configured to display computer generated images and / or information. In some cases, display device 944 may be configured to present a graphical user interface (GUI) to a user, wherein a user may interact with the graphical user interface. In some cases, a user may view a graphical user interface through display device 944. Additionally, or alternatively, processor 904 be connected to display device 944. In one or more embodiments, transmitting hypertension attributes 932a-c may include displaying hypertension attributes 932a-c at display device 944 using a visual interface. As used in this disclosure, a “graphical user interface” is a graphical form of user interface that allows users to interact with electronic devices. In some embodiments, a graphical user interface may include icons, menus, other visual indicators or representations (graphics), audio indicators such as primary notation, and display information and related user controls. A menu may contain a list of choices and may allow users to select one from them. A menu bar may be displayed horizontally across the screen such as pull-down menu. When any option is clicked in this menu, then the pull-down menu may appear. A menu may include a context menu that appears only when the user performs a specific action. An example of this is pressing the right mouse button. When this is done, a menu may appear under the cursor. Files, programs, web pages and the like may be represented using a small picture in a graphical user interface. For example, links to decentralized platforms as described in this disclosure may be incorporated using icons. Using an icon may be a fast way to open documents, run programs etc. because clicking on them yields instant access. With continued reference to FIG.9, as used in this disclosure, a “remote device” is any device external to computing device 912. Remote device 948 may transmit a signal, bit, datum, and / or parameter to computing device 912. Remote device 948 may include display device 944. Remote device 948 may receive hypertension attributes 932a-c and / or confidence score 940. Remote device 948 may include interactive features, wherein user may engage with the 88 Attorney Docket No.1518-126PCT1 information displayed on remote device 948. With continued reference to FIG.9, inputting patient time-series data 916 into hypertension panel 924 may include selecting hypertension models 928a-c from plurality of hypertension models as a function of a user input and the graphical user interface. In a non- limiting embodiment the graphical user interface may include a data structure. As used in this disclosure, “data structure” is a way of organizing data represented in a specialized format on a computer configured such that the information can be effectively presented in a graphical user interface. In some cases, the data structure includes any input data. In some cases, the data structure contains data and / or rules used to visualize the graphical elements within a graphical user interface. In some cases, the data structure may include any data described in this disclosure. In some cases, the data structure may be configured to modify the graphical user interface, wherein data within the data structure may be represented visually by the graphical user interface. In some cases, the data structure may be continuously modified and / or updated by processor 904, wherein elements within graphical user interface may be modified as a result. In some cases, processor 904 may be configured to transmit display device the data structure. Transmitting may include, and without limitation, transmitting using a wired or wireless connection, direct, or indirect, and between two or more components, circuits, devices, systems, and the like, which allows for reception and / or transmittance of data and / or signal(s) therebetween. Data and / or signals there between may include, without limitation, electrical, electromagnetic, magnetic, video, audio, radio, and microwave data and / or signals, combinations thereof, and the like, among others. Processor 904 may transmit hypertension attributes 932a-c, as described above, to a database wherein hypertension attributes 932a-c may be accessed from the database. Processor 904 may further transmit hypertension attributes 932a-c to display device 944, remote device 948, or another computing device 912. With continued reference to FIG.9, hypertension attributes 932a-c may include comparing hypertension attributes 932a-c to a target blood pressure level and displaying hypertension attributes 932a-c as a function of the comparison. As used in this disclosure, a target blood pressure level” is the ideal blood pressure of a healthy individual. The target blood pressure level may take various factors into account such as, but not limited to, the patient’s age, sex, height, weight, and the like. In an embodiment, the target blood pressure level of a patient may be 120 / 80 mm Hg. 89 Attorney Docket No.1518-126PCT1 With continued reference to FIG.9, graphical user interface may generate a visual element associated with hypertension attributes 932a-c, wherein the visual element is further associated with an event handler. As used in this disclosure, a “visual element” is any individual component that expresses an idea and / or conveys a message. A visual element may include visual data such as, but not limited to, images, colors, shapes, lines, arrows, icons, photographs, infographics, text, any combinations thereof, and the like. A visual element may include any data transmitted to display device, client device, and / or graphical user interface. In some embodiments, visual element may be interacted with. For example, visual element may include an interface, such as a button or menu. In some embodiments, visual element may be interacted with using a user device such as a smartphone, tablet, smartwatch, or computer. With continued reference to FIG.9, as used in this disclosure, an “event handler” is a module, data structure, function, and / or routine that performs an action on remote device in response to a user interaction with event handler graphic. For instance, and without limitation, an event handler may record data corresponding to user selections of previously populated fields such as drop-down lists and / or text auto-complete and / or default entries, data corresponding to user selections of checkboxes, radio buttons, or the like, potentially along with automatically entered data triggered by such selections, user entry of textual data using a keyboard, touchscreen, speech-to-text program, or the like. Event handler may generate prompts for further information, may compare data to validation rules such as requirements that the data in question be entered within certain numerical ranges, and / or may modify data and / or generate warnings to a user in response to such requirements. Exemplary embodiments have been disclosed above and illustrated in the accompanying drawings. It will be understood by those skilled in the art that various changes, omissions and additions may be made to that which is specifically disclosed herein without departing from the spirit and scope of the present invention. FIG.10A is an exemplary embodiment of a system, 1000a, for instantiating a hypertension classifier model in accordance with the subject disclosure. In one or more embodiments, system 1000a may include hypertension classifier model, 1004a. In one or more embodiments, hypertension classifier model 1004a may be trained using patient time-series data 1008. In one or more embodiments, hypertension classifier model 1004a may output hypertension attribute 1012a and confidence score 1016. In one or more embodiments, 90 Attorney Docket No.1518-126PCT1 hypertension classifier model 1004a may identify and determine that patient time-series data 1008 is associated with one or more hypertension levels, for example, without limitation, elevated blood pressure, stage 1 hypertension, stage 2 hypertension, critical hypertension. In one or more embodiments, hypertension classifier model 1004a may classify patient time-series data 1008 as either falling within a hypertension level or not, where if the patient does not yield a hypertension level, hypertension classifier model 1004a may suggest a different diagnosis, such as, without limitation a heart disease and / or condition related to hypertension. In one or more embodiments, confidence score 1016 may include an evaluation of the certainty of the hypertension based on various parameters. In one or more embodiments, confidence score 1016 may include a rating of certainty on a scale of 1 through 5, where 1 is confident and 5 is not confident at all. In one or more embodiments, hypertension attribute 1012a and / or confidence score 1016 may be displayed to the patient using a display device, wherein the display device may be a remote device as discussed previously in this disclosure. Without limitation, confidence score 1016 may be an additional output of the model in question, such as, hypertension classifier model 1004a, hypertension prediction model 1004b, and / or hypertension correlation model 1004c. Without limitation, confidence score 1016 may be evaluated based on a distance of the output from a given category of output, wherein the distance may be based on a degree to which the model converged during training, and the like. In an embodiment, the output may be represented using vectors, matrices, graphs, and any other data structure described herein. In In one or more embodiment, confidence score 1016 may be determined using outputs of other models, for example, in certain cases, outputs from different models might agree with the output of the model in question to a greater or lesser extent. In a non-limiting example, level of agreement among outputs may be determined by observing the degree of overlap between fuzzy sets, such as those described below. Continuing the non-limiting example, the shaded area of the figure pertaining to fuzzy sets illustrates the aforementioned degree of overlap between fuzzy sets, where the more overlap there is between fuzzy sets, the greater the level of agreement among outputs, and the less overlap there is between fuzzy sets, the lesser the level of agreement among outputs. In an embodiment, the level of agreement among outputs may be produced by another model, wherein the other model’s function is to generate confidence scores for each model output based on comparisons with other model outputs. FIG.10B is an exemplary embodiment of a system, 1000b, for instantiating a 91 Attorney Docket No.1518-126PCT1 hypertension prediction model in accordance with the subject disclosure. In one or more embodiments, system 1000b may include hypertension prediction model, 1004b. In one or more embodiments, hypertension prediction model 1004b may be trained using patient time-series data 1008. In one or more embodiments, hypertension prediction model 1004b may output hypertension attribute 1012b and confidence score 1016. In one or more embodiments, hypertension prediction model 1004b may provide a prediction of the patients current blood pressure based on a tolerance of the output (e.g., hypertension attribute 1012b) accuracy. In one or more embodiments, the tolerance threshold of the output from hypertension prediction model 1004b may be based on the device used to capture the patient time-series data 1008, as well as other parameters and / or assumptions of the hypertension prediction model 1004b. In one or more embodiments, hypertension prediction model 1004b may provide the patient with a prediction of patient’s future blood pressure level in a specified amount of time. For example, without limitation, hypertension prediction model 1004b may provide a prediction of patient’s blood pressure levels 5 years from the current date based on a plurality of factors including, but not limited to, the patient’s age, sex, diet, exercise routines, and the like. In one or more embodiments, confidence score 1016 may include an evaluation of the certainty of the hypertension attribute 1012b based on various parameters. In one or more embodiments, confidence score 1016 may include a rating of certainty on a scale of 1 through 5, where 1 is confident and 5 is not confident at all. In one or more embodiments, hypertension attribute 1012b and / or confidence score 1016 may be displayed to the patient using a display device, wherein the display device may be a remote device as discussed previously in this disclosure. FIG.10C is an exemplary embodiment of a system, 1000c, for instantiating a hypertension correlation model in accordance with the subject disclosure. In one or more embodiments, system 1000c may include hypertension correlation model, 1004c. In one or more embodiments, hypertension correlation model 1004c may be trained using electronic health records 1020. In one or more embodiments, hypertension correlation model 1004c may receive patient time-series data 1008 as input. In one or more embodiments, hypertension correlation model 1004c may generate hypertension attribute 1012c and confidence score 1016 as outputs. In one or more embodiments, electronic health records 1020 may include a plurality of ECG signal data and / or ECG electronic image data. In one or more embodiments, hypertension correlation model 1004c may analyze electronic health records 1020, segment portions of the 92 Attorney Docket No.1518-126PCT1 ECG signal data to define a particular signal characteristic, generate a label for the segmented portion of the ECG signal data, and generate hypertension attribute 1012c. In one or more embodiments, confidence score 1016 may include a rating of certainty on a scale of 1 through 5, where 1 is confident and 5 is not confident at all. In one or more embodiments, hypertension attribute 1012b and / or confidence score 1016 may be displayed to the patient using a display device, wherein the display device may be a remote device as discussed previously in this disclosure. Referring now to FIG.11, a flow diagram of an exemplary method 1100 detecting hypertension attributes in a patient time-series data is illustrated. At step 1105, method 1100 includes receiving, using at least a processor, a patient time-series data associated with a patient, wherein the patient time-series data is captured using a measurement device. This may be implemented as described and with reference to FIGS.1-6. Still referring to FIG.11, at step 1110, method 1100 includes inputting, using at least a processor, the patient time-series data into a hypertension panel wherein the hypertension panel may include of a plurality of hypertension models. In an embodiment, the hypertension model may include a matrix. In another non-limiting embodiment, the hypertension panel is configured to receive the patient time-series data as an input, calculate the hypertension attribute, and output the hypertension attribute. In another embodiment, the plurality of hypertension models comprise a hypertension classifier model, a hypertension prediction model, and a hypertension correlation model. In another embodiment, training the hypertension model may include receiving a plurality of a patient time-series data associated with a plurality of patients, pretraining hypertension model in the hypertension panel as a function of the plurality of patient time-series data examples by adjusting one or more parameter attributes of the hypertension model, and training the hypertension model as a function of the parameter attributes and an electronic health records. In an embodiment, inputting the patient time-series data into the hypertension panel may include selecting the hypertension model from a plurality of hypertension models as a function of a patient input and the graphical user interface. This may be implemented as described and with reference to FIGS.1-6. Still referring to FIG.11, at step 1115, method 1100 includes generating, using at least a processor, the hypertension attribute from the hypertension panel as a function of the patient time-series data and a hypertension model, wherein the hypertension attribute may include 93 Attorney Docket No.1518-126PCT1 generating, using a first hypertension model, a first hypertension attribute comprising a measurement of the patient yielding a first hypertension level, generating, using a second hypertension model, a second hypertension attribute comprising a measurement of the patient yielding a second hypertension level, and generating the hypertension attribute using the patient time-series data and at least one of the first hypertension model and the second hypertension model. In another non-limiting embodiment, the hypertension level may include a systolic blood pressure and a diastolic blood pressure. In an embodiment, the hypertension attribute may include a hypertension deviation, wherein the hypertension deviation may include a change in the hypertension attribute. This may be implemented as described and with reference to FIGS.1- 6. Still referring to FIG.11, at step 1120, method 1100 includes generating, using at least a processor, a confidence score from the hypertension panel as a function of the patient time-series data and at least one of the first hypertension model and the second hypertension model. This may be implemented as described and with reference to FIGS.1-6. At a high level, aspects of the present disclosure are directed to an apparatus and a method for identifying the progression of coronary heart disease. The apparatus includes at least processor and a memory communicatively connected to the processor. The memory instructs the processor to receive a subject profile associated with a subject, wherein the subject profile comprises a plurality of electrocardiogram (ECG) data. The memory instructs the processor to identify contextual data as a function of the subject profile. The memory instructs the processor to generate a set of cardiac scores as function of the contextual data and the plurality of ECG data using a set of cardiac machine learning models. The memory instructs the processor to select at least one stage of coronary heart disease from a plurality of stages of coronary heart disease of the subject as a function of the set of cardiac scores. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples. Referring now to FIG.12, an exemplary embodiment of an apparatus 1200 for identifying the progression of coronary heart disease is illustrated. Apparatus 1200 includes a processor 1204. Processor 1204 may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and / or system on a chip (SoC) as described in this disclosure. Computing 94 Attorney Docket No.1518-126PCT1 device may include, be included in, and / or communicate with a mobile device such as a mobile telephone or smartphone. Processor 1204 may include a single computing device operating independently, or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices. Processor 1204 may interface or communicate with one or more additional devices as described below in further detail via a network interface device. Network interface device may be utilized for connecting processor 1204 to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone / voice provider (e.g., a mobile communications provider data and / or voice network), a direct connection between two computing devices, and any combinations thereof. A network may employ a wired and / or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software etc.) may be communicated to and / or from a computer and / or a computing device. Processor 1204may include but is not limited to, for example, a computing device or cluster of computing devices in a first location and a second computing device or cluster of computing devices in a second location. Processor 1204 may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. Processor 1204 may distribute one or more computing tasks as described below across a plurality of computing devices of computing device, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices. Processor 1204 may be implemented using a “shared nothing” architecture in which data is cached at the worker, in an embodiment, this may enable scalability of apparatus 1200 and / or computing device. With continued reference to FIG.12, processor 1204 may be designed and / or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, processor 1204 may be configured to perform a single step or sequence repeatedly until a desired or commanded 95 Attorney Docket No.1518-126PCT1 outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and / or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and / or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and / or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Processor 1204 may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and / or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and / or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and / or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and / or parallel processing. With continued reference to FIG.12, apparatus 1200 includes a memory. Memory is communicatively connected to processor 1204. Memory may contain instructions configuring processor 1204 to perform tasks disclosed in this disclosure. As used in this disclosure, “communicatively connected” means connected by way of a connection, attachment, or linkage between two or more relata which allows for reception and / or transmittance of information therebetween. For example, and without limitation, this connection may be wired or wireless, direct, or indirect, and between two or more components, circuits, devices, systems, apparatus, and the like, which allows for reception and / or transmittance of data and / or signal(s) therebetween. Data and / or signals therebetween may include, without limitation, electrical, electromagnetic, magnetic, video, audio, radio, and microwave data and / or signals, combinations thereof, and the like, among others. A communicative connection may be achieved, for example, and without limitation, through wired or wireless electronic, digital, or analog, communication, either directly or by way of one or more intervening devices or components. Further, communicative connection may include electrically coupling or connecting at least an output of one device, component, or circuit to at least an input of another device, component, or circuit. For example, without limitation, via a bus or other facility for intercommunication between elements of a computing device. Communicative connecting may also include indirect connections via, for example, and without limitation, wireless connection, radio communication, 96 Attorney Docket No.1518-126PCT1 low power wide area network, optical communication, magnetic, capacitive, or optical coupling, and the like. In some instances, the terminology “communicatively coupled” may be used in place of communicatively connected in this disclosure. With continued reference to FIG 1, processor 1204 may be configured to receive a subject profile 1208 from a subject. For the purposes of this disclosure, an “subject profile” is a data structure including data about a subject. A subject profile 1208 may describe personal information regarding the subject. In a non-limiting embodiment, a subject profile 1208 may describe the subject’s height, weight, sex, age, name, contact information, medical history, and the like. A subject profile 1208 may also include testing results from various medical tests. This may include CT scans, ECGs, EKGs, Observations of a medical professional, familial history, vital signs of the subject, and the like. Subject profiles 1208 may be initiated and updated by either the processor itself, the subject, a third party, electronic medical records, and the like indicating a versatile system that integrates data from multiple sources to maintain up-to-date and accurate profiles. With continued reference to FIG 1, processor 1204 a subject profile 1208 may include a plurality of electrocardiogram (ECG) data 1212 from a subject. As used in the current disclosure, a “electrocardiogram data” is a signal representative of the electrical activity of the heart. The ECG data 1212 may consist of several distinct waves and intervals, each representing a different phase of the cardiac cycle. These waves may include the P-wave, QRS complex, T wave, U wave, and the like. The P-wave may represent atrial depolarization (contraction) as the electrical impulse spreads through the atria. The QRS complex may represent ventricular depolarization (contraction) as the electrical impulse spreads through the ventricles. The QRS complex may include three waves: Q wave, R wave, and S wave. The T-wave may represent ventricular repolarization (recovery) as the ventricles prepare for the next contraction. The U-wave may sometimes be present after the T wave, it represents repolarization of the Purkinje fibers. The intervals between these waves provide information about the duration and regularity of various phases of the cardiac cycle. The ECG data 1212 can be used to help diagnose various heart conditions, such as arrhythmias, myocardial infarction (heart attack), conduction abnormalities, electrolyte imbalances, coronary heart disease, and the like. In an embodiment, each sensor may generate an individual ECG data 1212. With continued reference to FIG 1, the plurality of electrocardiogram data 1212 may be 97 Attorney Docket No.1518-126PCT1 generated using at least a sensor. As used in this disclosure, a “sensor” is a device that is configured to detect an input and / or a phenomenon and transmit information related to the detection. Sensor may detect a plurality of data. A plurality of data detected by sensor may include, but is not limited to, electrocardiogram data 1212, heart rate, blood pressure, electrical signals related to the heart, and the like. In one or more embodiments, and without limitation, sensor may include a plurality of sensors. At least a sensor may include an ECG machine. In one or more embodiments, and without limitation, sensor may include one or more electrodes, and the like. Electrodes used for an electrocardiogram (ECG) are small sensors or conductive patches that are placed on specific locations on the body to detect and record the electrical signals generated by the heart. Senor serves as the interface between the body and the ECG machine, allowing for the measurement and recording of the heart's electrical activity. A plurality of sensors may include 10 electrodes used for a standard 12-lead ECG, placed in specific positions on the chest and limbs of the patient. Sensors may also include various lead systems including, 1- lead, 2 -lead, 6-leads, 12, leads, standard limb leads, augmented limb leads, pectoral leads, and the like. These electrodes are typically made of a conductive material, such as metal or carbon, and are connected to lead wires that transmit the electrical signals to the ECG machine for recording. Proper electrode placement may be crucial to ensure accurate signal detection and recording. A number of electrodes used by an ECG machine may depend on a particular machine in use and may vary from a single electrode on a wearable device to twelve or more electrodes, or any number in between. With continued reference to FIG 1, the plurality of sensors may be placed on each limb, wherein there may be at least one sensor on each arm and / or leg of the subject. These sensors may be labeled I, II, III, V1, V2, V3, V4, V5, V6, and the like. For example, Sensor I may be placed on the left arm, Sensor II may be placed on the right arm, and Sensor III may be placed on the left leg. Additionally, a plurality of sensors may be placed on various portions of the patient’s torso and chest. For example, a sensor V1 may be placed in the fourth intercostal space at both the right sternal borders and sensor V2 may be fourth intercostal space at both the left sternal borders. A sensor V3 may also be placed between sensors V2 and V4, halfway between their positions. Sensor V4 may be placed in the fifth intercostal space at the midclavicular line. Sensor V5 may be placed horizontally at the same level as sensor V4 but in the anterior axillary line. Sensor V6 may be placed horizontally at the same level as V4 and V5 but in the midaxillary line. 98 Attorney Docket No.1518-126PCT1 With continued reference to FIG 1, the plurality of sensors may include augmented unipolar sensors. These sensors may be labeled as aVR, aVL, and aVF. These sensor may be derived from the limb sensors and provide additional information about the heart's electrical activity. These leads are calculated using specific combinations of the limb leads and help assess the electrical vectors in different orientations. For example, aVR may be derived from Sensor II and Sensor III. In another example, aVL may be derived from sensor I and Sensor III. Additionally, aVF may be derived from Lead I and Lead II. The combination of limb sensors, precordial sensors, and augmented unipolar sensors allows for a comprehensive assessment of the heart's electrical activity in three dimensions. With continued reference to FIG 1, processor 1204 may be configured to receive subject profile 1208 from an electronic health records (EHR) 1216. As used in the current disclosure, “electronic health record” is a data structure which includes a collection of a health data associated with the subject. As used in the current disclosure, “health data” refers to the collection of information related to a patient's health and healthcare. Health data may include elements of data regarding treatment records, medical history, laboratory results, radiology reports, medical records, clinical notes, and the like. An electronic health record (EHR) 1216 may be a digital version of a patient's medical information that is stored and managed in a computerized system. It may be a comprehensive, longitudinal collection of a patient's health- related data that includes medical history, diagnoses, medications, treatment plans, test results, and other relevant health information. EHRs 1216 may contain a wide range of patient information, including personal demographics, medical history, allergies, immunizations, medications, laboratory results, imaging reports, surgical procedures, and progress notes. This comprehensive data may provide a complete overview of a patient's health and facilitates informed decision-making. EHRs 1216 may include a patient's past and current medical conditions, surgeries, allergies, immunization records, medications, symptoms, medical observations, and any significant health events. EHRs 1216 may additionally include a large amount of information regarding the patient's health background. This may include previous diagnosis, medical tests, medical imaging, and the like. EHRs may include documentation, observations, assessments, and treatment plans from medical professionals. This may include progress notes, discharge summaries, and other relevant clinical documentation. EHRs may include information related to prescribed medications, including dosage, frequency, symptoms, 99 Attorney Docket No.1518-126PCT1 effect, and duration. EHRs may include test results, which may include laboratory test results, radiology reports, medical imaging reports, and other diagnostic imaging findings. With continued reference to FIG 1, EHRs 1216 may be received by processor 1204 via user input. For example, and without limitation, the user or a third party may manually input EHRs 1216 using a graphical user interface of processor 1204 or a remote device, such as for example, a smartphone or laptop. EHRs 1216 may additionally be generated via the answer to a series of questions. In a non-limiting embodiment, a user may be prompted to input specific information or may fill out a questionnaire. In an embodiment, a graphical user interface may display a series of questions to prompt a user for information pertaining to the EHRs 1216. The EHRs 1216 may be transmitted to processor 1204, such as via a wired or wireless communication, as previously discussed in this disclosure. With continued reference to FIG.12, plurality of EHRs 1216 may include a plurality of metadata. As used in the current disclosure, “metadata” refers to descriptive information or attributes that provide context, structure, and meaning to data. Metadata is essentially data about data. Metadata may help in understanding and managing various aspects of data, such as its origin, content, format, quality, and usage. It may play a crucial role in organizing, searching, and interpreting data effectively. Metadata may include descriptive metadata, structural metadata, administrative metadata, technical metadata, provenance metadata, usage metadata, and the like. Metadata may be organized and managed through metadata schemas, standards, or frameworks. These provide guidelines and specifications for capturing, storing, and exchanging metadata in a consistent and structured manner. Common metadata standards include Dublin Core, Metadata Object Description Schema (MODS), and the Federal Geographic Data Committee (FGDC) metadata standard. In some cases, metadata may be associated with textual data or image data. In some cases, metadata may include data associated with the health of the patient and the patient’s medical records. Metadata includes patient-specific information such as unique identifiers (e.g., medical record number, national identification number), patient demographics (name, date of birth, gender), contact details, and emergency contact information. These identifiers help in linking and identifying the EHR of individual patients. Each entry or update in an EHR may be accompanied by a date and time stamp. This metadata may be captured when the event or documentation took place, allowing processor 1204 to track the chronological order of patient encounters, treatments, test results, and other relevant information. 100 Attorney Docket No.1518-126PCT1 Metadata may include details about the healthcare professional or user who created or updated a specific EHR entry. This information may include the name, credentials, role, and department of the author. It helps in accountability, audit trails, and ensuring data integrity. Metadata may indicate the source of the EHR data, whether it was entered directly by a healthcare provider, imported from a laboratory or diagnostic system, received from external healthcare organizations, or captured through patient-generated sources (e.g., wearables, patient-reported data). Metadata may include information regarding access permissions, user roles, and security settings associated with the EHR 1216. This metadata may be used to enforce privacy and security protocols, ensuring that only authorized individuals can view, modify, or access specific portions of the EHR. Metadata may contain notes, comments, or observations made by a medical professional. These annotations might highlight specific features, anomalies, or noteworthy aspects of the EHR. The date and time when the slide was prepared, analyzed, or labeled can be associated as metadata. With continued reference to FIG 1, the EHR 1216 may include a plurality of multi-modal data associated with a subject. As used in the current disclosure, “multi-modal data” is data which includes a plurality of modalities data. Modalities of data may include images, text, audio, documents, electronic health records, sensor data, and the like. Multi-modal data may include textual data. As used in the current disclosure, “textual data” is a collection of data that consists of text-based information. Textual data may include any written information, such as documents, emails, notes, handwriting, chat conversations, and the like. Examples of textual data may include documents, captions, sentences, paragraphs, free-text fields, transcriptions, prognostic labels, and the like. Textual data may include data from a plurality of digital or handwritten notes. Notes may be written by a medical professional. The notes may depict conditions of the patient. Textual data may be associated with electronic health records (EHRs). Textual data may refer to the written or typed information that is recorded and stored as part of a patient's health record in a digital format. It includes a wide range of textual information that provides details about the patient's medical history, diagnoses, treatments, procedures, medications, observations, clinical notes, and other relevant healthcare information. Multi-modal data may include image data. As used in the current disclosure, “image data” is a collection of data that consists of data associated with a plurality of images. Image data encompasses visual representations captured through cameras or generated through medical imaging, graphs, microscopes, or other image 101 Attorney Docket No.1518-126PCT1 capturing systems. Image data associated with electronic health records (EHRs) refers to the visual information that is linked or integrated with the patient's health record. It includes medical images such as X-rays, CT scans, MRI scans, ultrasound images, endoscopy images, pathology slides, and other types of diagnostic or clinical images. With continued reference to FIG.12, processor 1204 may be configured to receive a subject profile 1208 or an EHR 1216 using an application programming interface (API). As used herein, an “application programming interface” is a set of functions that allow applications to access data and interact with external software components, operating systems, or microdevices, such as another web application or computing device. An API may define the methods and data formats that applications can use to request and exchange information. APIs enable seamless integration and functionality between different systems, applications, or platforms. An API may deliver subject profile 1208 or an EHR 1216 to apparatus 1200 from a system / application that is associated with a subject, medical provider, or other third-party custodian of subject information. In an embodiment, an API may be configured to query for web applications or other websites to retrieve a subject profile 1208 or an EHR 1216. An API may be further configured to filter through web applications according to a filter criterion. In this disclosure, “filter criteria” are conditions the web applications must fulfill in order to qualify for API. Web applications may be filtered based off these filter criteria. Filter criterion may include, without limitation, web application dates, web application traffic, web application types, web applications addresses, and the like. Once an API filters through web applications according to a filter criterion, it may select a web application. Processor 1204 may transmit, through the API, ECG data 1212 to apparatus 1200. API may further automatically fill out user entry fields of the web application with the user credentials in order to gain access to the ECG data 1212. Web applications may include, without limitation, a medical database, hospital website, file scanning, email programs, third party websites, governmental websites, or the like. With continued reference to FIG.12, subject profile 1208 and / or EHR 1216 may be received from a user using a chatbot. A chatbot can be used to receive inputs from a user to generate subject data, wherein a chatbot input is discussed in greater detail herein below. The chatbot may be configured to ask a user a plurality of inquiries related to one or more aspects of their business. The chatbot may use natural language processing techniques to understand and extract key information from the user's responses. This may help in determining the specific 102 Attorney Docket No.1518-126PCT1 symptoms and / or medical conditions of the subject. In a non-limiting example, a chatbot may be used to gather information regarding the subject’s family medical history or the subject medical history. In some cases, a chatbot may be used to gather more information related to the subject’s current symptoms. A chatbot may present the user with inquiries regarding duration or severity of the subject’s symptoms. In some cases, the collected data and user inputs may be used to generate a structured subject profile 1208. Processor 1204 may organize the information into different sections or categories based on the nature of the entity. This may be done using a chatbot as described herein below in FIG 7. With continued reference to FIG.12, a subject profile 1208 and / or EHR 1216 may be generated from one or more medical records. As used in the current disclosure, a “medical record” is a document that contains information regarding the subject’s medical history. Medical records may include data about any medical procedures, medical tests, medical images, observations of a medical professional, prescription history, diagnostic history, government records (i.e., birth certificates, social security cards, and the like), and the like of the subject. Medical records may be identified using a web crawler. Medical records may include a variety of types of "notes" entered over time by a medical professional. Medical records may be converted into machine-encoded text using an optical character reader (OCR). Still referring to FIG.12, in some embodiments, optical character recognition or optical character reader (OCR) includes automatic conversion of images of written (e.g., typed, handwritten, or printed text) into machine-encoded text. In some cases, recognition of at least a keyword from an image component may include one or more processes, including without limitation optical character recognition (OCR), optical word recognition, intelligent character recognition, intelligent word recognition, and the like. In some cases, OCR may recognize written text, one glyph or character at a time. In some cases, optical word recognition may recognize written text, one word at a time, for example, for languages that use a space as a word divider. In some cases, intelligent character recognition (ICR) may recognize written text one glyph or character at a time, for instance by employing machine learning processes. In some cases, intelligent word recognition (IWR) may recognize written text, one word at a time, for instance by employing machine learning processes. Still referring to FIG.12, in some cases, OCR may be an "offline" process, which analyses a static document or image frame. In some cases, handwriting movement analysis can 103 Attorney Docket No.1518-126PCT1 be used as input for handwriting recognition. For example, instead of merely using shapes of glyphs and words, this technique may capture motions, such as the order in which segments are drawn, the direction, and the pattern of putting the pen down and lifting it. This additional information can make handwriting recognition more accurate. In some cases, this technology may be referred to as “online” character recognition, dynamic character recognition, real-time character recognition, and intelligent character recognition. Still referring to FIG.12, in some cases, OCR processes may employ pre-processing of image components. Pre-processing process may include without limitation de-skew, de-speckle, binarization, line removal, layout analysis or “zoning,” line and word detection, script recognition, character isolation or “segmentation,” and normalization. In some cases, a de-skew process may include applying a transform (e.g., homography or affine transform) to the image component to align text. In some cases, a de-speckle process may include removing positive and negative spots and / or smoothing edges. In some cases, a binarization process may include converting an image from color or greyscale to black-and-white (i.e., a binary image). Binarization may be performed as a simple way of separating text (or any other desired image component) from the background of the image component. In some cases, binarization may be required for example if an employed OCR algorithm only works on binary images. In some cases, a line removal process may include the removal of non-glyph or non-character imagery (e.g., boxes and lines). In some cases, a layout analysis or “zoning” process may identify columns, paragraphs, captions, and the like as distinct blocks. In some cases, a line and word detection process may establish a baseline for word and character shapes and separate words, if necessary. In some cases, a script recognition process may, for example in multilingual documents, identify a script allowing an appropriate OCR algorithm to be selected. In some cases, a character isolation or “segmentation” process may separate signal characters, for example, character-based OCR algorithms. In some cases, a normalization process may normalize the aspect ratio and / or scale of the image component. Still referring to FIG.12, in some embodiments, an OCR process will include an OCR algorithm. Exemplary OCR algorithms include matrix-matching process and / or feature extraction processes. Matrix matching may involve comparing an image to a stored glyph on a pixel-by-pixel basis. In some cases, matrix matching may also be known as “pattern matching,” “pattern recognition,” and / or “image correlation.” Matrix matching may rely on an input glyph 104 Attorney Docket No.1518-126PCT1 being correctly isolated from the rest of the image component. Matrix matching may also rely on a stored glyph being in a similar font and at the same scale as input glyph. Matrix matching may work best with typewritten text. Still referring to FIG.12, in some embodiments, an OCR process may include a feature extraction process. In some cases, feature extraction may decompose a glyph into features. Exemplary non-limiting features may include corners, edges, lines, closed loops, line direction, line intersections, and the like. In some cases, feature extraction may reduce dimensionality of representation and may make the recognition process computationally more efficient. In some cases, extracted features can be compared with an abstract vector-like representation of a character, which might reduce to one or more glyph prototypes. General techniques of feature detection in computer vision are applicable to this type of OCR. In some embodiments, machine- learning processes like nearest neighbor classifiers (e.g., k-nearest neighbors algorithm) can be used to compare image features with stored glyph features and choose a nearest match. OCR may employ any machine-learning process described in this disclosure, for example machine- learning processes described with reference to FIGS.5-7. Exemplary non-limiting OCR software includes Cuneiform and Tesseract. Cuneiform is a multi-language, open-source optical character recognition system originally developed by Cognitive Technologies of Moscow, Russia. Tesseract is free OCR software originally developed by Hewlett-Packard of Palo Alto, California, United States. Still referring to FIG.12, in some cases, OCR may employ a two-pass approach to character recognition. The second pass may include adaptive recognition and use letter shapes recognized with high confidence on a first pass to recognize better remaining letters on the second pass. In some cases, two-pass approach may be advantageous for unusual fonts or low- quality image components where visual verbal content may be distorted. Another exemplary OCR software tool include OCRopus. OCRopus development is led by German Research Centre for Artificial Intelligence in Kaiserslautern, Germany. In some cases, OCR software may employ neural networks, for example neural networks as taught in reference to FIGS.2, 4, and 5. Still referring to FIG.12, in some cases, OCR may include post-processing. For example, OCR accuracy can be increased, in some cases, if output is constrained by a lexicon. A lexicon may include a list or set of words that are allowed to occur in a document. In some cases, a lexicon may include, for instance, all the words in the English language, or a more technical 105 Attorney Docket No.1518-126PCT1 lexicon for a specific field. In some cases, an output stream may be a plain text stream or file of characters. In some cases, an OCR process may preserve an original layout of visual verbal content. In some cases, near-neighbor analysis can make use of co-occurrence frequencies to correct errors, by noting that certain words are often seen together. For example, “Washington, D.C.” is generally far more common in English than “Washington DOC.” In some cases, an OCR process may make use of a priori knowledge of grammar for a language being recognized. For example, grammar rules may be used to help determine if a word is likely to be a verb or a noun. Distance conceptualization may be employed for recognition and classification. For example, a Levenshtein distance algorithm may be used in OCR post-processing to further optimize results. With continued reference to FIG.12, a subject profile 1208 and / or EHR 1216 may be generated using a web crawler. A “web crawler,” as used herein, is a program that systematically browses the internet for the purpose of web indexing. The web crawler may be seeded with platform URLs, wherein the crawler may then visit the next related URL, retrieve the content, index the content, and / or measures the relevance of the content to the topic of interest. In some embodiments, processor 1204 may generate a web crawler to compile the subject profile 1208 and / or EHR 1216. The web crawler may be seeded and / or trained with a reputable website, such as the subject’s business website, to begin the search. A web crawler may be generated by a processor 1204. In some embodiments, the web crawler may be trained with information received from a user through a user interface. In some embodiments, the web crawler may be configured to generate a web query. A web query may include search criteria received from a user. For example, a user may submit a plurality of websites for the web crawler to search to extract data related to the subject profile 1208, past subject profiles 1208, EHRs 1216, based on criteria such as a time, location, and the like. In some cases, a web crawler may be seeded with the website to the entities website. The process of seeding a web crawler refers to the process of providing an initial set of URLs or starting points from which the crawler begins its exploration of the web. These initial URLs are often called seed URLs or a seed set. Seeding may be a curtail step in the web crawling process as it defines the starting point for discovering and indexing web pages. With continued reference to FIG 1, processor 1204 may identify contextual data 1220 as a function of the subject profile 1208. As used in the current disclosure, “contextual data” refers 106 Attorney Docket No.1518-126PCT1 to details about the subject that provide a more comprehensive understanding of the ECG data 1212. Contextual data 1220 associated with the subject profile 1208 may be information that surrounds and gives meaning to that data. This additional information may assist the apparatus 1200 in interpreting and comprehending the ECG data 1212. The use of contextual data 1220 may provide a more individualized healthcare diagnosis. By accounting for the personal and medical nuances of each subject, the system may facilitate a deeper and more accurate understanding of heart health, directly influencing the precision of diagnostics. These patient- specific factors can influence the interpretation of ECG readings and the subsequent diagnosis. In an embodiment, contextual data 1220 may include the patient's medical history, presenting symptoms, risk factors for CHD, and any previous cardiac events or procedures. Medical history that is particularly relevant includes conditions like diabetes, hypertension, hyperlipidemia, heart disease, family history, all of which increase the likelihood of CHD. Additionally, symptoms such as chest pain, shortness of breath, and episodes of syncope or palpitations can provide critical clues that guide the interpretation of the ECG. For instance, certain patterns of chest pain might suggest ischemia, which could correlate with specific changes in the ECG like ST-segment elevations or depressions. The patient’s age and sex also play a significant role, as ECG norms can vary across different demographics, and the pattern of heart disease may present differently. Risk factors such as smoking, obesity, and a sedentary lifestyle further contribute to the overall assessment and interpretation of the ECG. The presence of these risk factors in conjunction with ECG abnormalities may strengthen the suspicion of CHD. Previous ECGs, if available, are invaluable as they provide a baseline for comparison, helping to identify new changes that may indicate acute coronary events or progression of disease. With continued reference to FIG 1, processor 1204 may identify contextual data 1220 from a subject profile 1208. Processor 1204 may access the subject profile 1208, which may include a wide range of information such as demographic details, medical history, current and past medical conditions, lifestyle choices, genetic data, and previous health assessments. Utilizing algorithms designed for data extraction and interpretation, the processor may scan through this information to highlight relevant data points that are crucial for assessing the subject’s health status. This might involve categorizing data into different health-related themes such as cardiovascular risk factors, chronic conditions, or lifestyle habits. The processor may then apply predefined rules or machine learning models to analyze and prioritize this data based 107 Attorney Docket No.1518-126PCT1 on its relevance and impact on the subject’s health. For example, it might flag high blood pressure and family history of heart disease as significant risk factors for coronary heart disease. This contextual information may then be organized and made readily accessible for further analysis or to inform decision-making processes. With continued reference to FIG 1, the identification of contextual data 1220 may include analyzing and interpreting textual data or other data elements associated with a subject profile. Processor 1204 may identify textual data or other data elements associated with a subject profile 1208 using natural language processing (NLP) techniques, such as tokenization, part-of-speech tagging, and named entity recognition (NER), may be employed to understand the structure, and meaning of the text. With continued reference to FIG 1, identifying contextual data 1220 within the at least a subject profile 1208 may include generating a plurality of named entities and / or keyword sets associated with the subject profile 1208 using a natural language processing model. As used in the current disclosure, a “natural language processing (NLP) model” is a computational model designed to process and understand human language. It leverages techniques from machine learning, linguistics, and computer science to enable computers to comprehend, interpret, and generate natural language text. The NLP model may preprocess the textual data, wherein the input text may include all text contained within the subject profile 1208, or any other data mentioned herein. Preprocessing the input text may involve tasks like tokenization (splitting text into individual words or sub-word units), normalizing the text (lowercasing, removing punctuation, etc.), and encoding the text into a numerical representation suitable for the model. The NLP model may include transformer architecture, wherein the transformers may be deep learning models that employ attention mechanisms to capture the relationships between words or sub-word units in a text sequence. They consist of multiple layers of self-attention and feed- forward neural networks. The NLP model may weigh the importance of different words or sub- word units within a text sequence while considering the context. It may enable the model to capture dependencies and relationships between words, considering both local and global contexts. This process may be used to identify a plurality of named entities. With continued reference to FIG 1, a language processing model may include a named entity recognition system to produce associations between one or more significant terms extracted from the subject profile 1208 and detect associations, including without limitation 108 Attorney Docket No.1518-126PCT1 mathematical associations, between such significant terms. Associations between language elements, where language elements include for purposes herein extracted significant terms, relationships of such categories...

Claims

WHAT IS CLAIMED IS:

1. An apparatus for tracking cardiac indices, the apparatus comprising: a processor; and a memory communicatively connected to the processor, wherein the memory contains instructions configurating the processor to: receive cardiac input data from a patient comprising a plurality of cardiac signals; input the cardiac input data into a cardiac panel, the cardiac panel comprising a plurality of cardiac models, wherein: at least one cardiac model of the cardiac panel is configured to calculate a cardiac index associated with a heart condition; the at least one cardiac model comprises at least one cardiac machine learning model that has been trained and configured to receive cardiac input data as inputs and output cardiac indices; and the at least one cardiac model is configured to calculate a cardiac index associated with diastolic dysfunction; and generate one or more cardiac indices from the cardiac panel as a function of the cardiac input data and the at least one cardiac machine learning model, wherein at least one cardiac index of the one or more cardiac indices comprises a probability of the patient satisfying at least one grading threshold.

2. The apparatus of claim 1, wherein the cardiac input data includes at least one electrocardiogram (ECG).

3. The apparatus of claim 1, wherein training the at least one cardiac machine learning model comprises: receiving a plurality of cardiac training data associated with a plurality of patients; pretraining the at least one cardiac machine learning model as a function of the plurality of cardiac training data by adjusting one or more parameters within the at least one cardiac machine learning model; and retraining the at least one cardiac machine learning model as a function of the one or more parameters and a labeled subset of the plurality of cardiac training data.

4. The apparatus of claim 3, wherein the at least one cardiac model includes a classification 212 Attorney Docket No.1518-126PCT1model that classifies a case of diastolic dysfunction under one category of a plurality of categories.

5. The apparatus of claim 1, wherein: at least one cardiac index of the one or more cardiac indices includes an elevated left ventricular filling pressure; and the at least one grading threshold includes a grading threshold in elevated left ventricular filling pressure.

6. The apparatus of claim 1, wherein: at least one cardiac index of the one or more cardiac indices is associated with pulmonary hypertension; and the at least one grading threshold includes a grading threshold associated with pulmonary hypertension.

7. The apparatus of claim 1, wherein: inputting the cardiac input data into the cardiac panel comprises inputting a first plurality of time series data containing the cardiac input data; and generating the one or more cardiac indices comprises generating a second plurality of time series data containing the one or more cardiac indices.

8. The apparatus of claim 7, wherein at least one of the one or more cardiac indices includes a cardiac index deviation.

9. The apparatus of claim 1, wherein generating the one or more cardiac indices comprises: receiving at least one user update; and updating at least one cardiac index of the one or more cardiac indices as a function of the at least one user update.

10. The apparatus of claim 1, wherein generating the at least one cardiac index of the one or more cardiac indices comprises: comparing the one or more cardiac indices to one or more cardiac baselines; calculating one or more distance metrics as a function of the comparison; and generating the one or more cardiac indices as a function of at least one distance metric of the one or more distance metrics.

11. The apparatus of claim 10, wherein the processor is further configured to display at least one cardiac index of the one or more cardiac indices through a user interface. 213 Attorney Docket No.1518-126PCT112. The apparatus of claim 11, wherein displaying the at least one cardiac index of the one or more cardiac indices further comprises generating a color-coded visualization as a function of the at least one cardiac index of the one or more cardiac indices and the at least one distance metric of the one or more distance metrics.

13. The apparatus of claim 1, wherein the processor is further configured to predict a projected cardiac index as a function of the cardiac input data and the at least one cardiac machine learning model.

14. A method for tracking cardiac indices, the method comprising: receiving, by a processor, cardiac input data from a patient comprising a plurality of cardiac signals; inputting, by the processor, the cardiac input data into a cardiac panel, the cardiac panel comprising a plurality of cardiac models, wherein: at least one cardiac model of the cardiac panel is configured to calculate a cardiac index associated with a heart condition; the at least one cardiac model comprises at least one cardiac machine learning model that has been trained and configured to receive cardiac input data as inputs and output cardiac indices; and the at least one cardiac model is configured to calculate a cardiac index associated with diastolic dysfunction; and generating, by the processor, one or more cardiac indices from the cardiac panel as a function of the cardiac input data and the at least one cardiac machine learning model, wherein at least one cardiac index of the one or more cardiac indices comprises a probability of the patient satisfying at least one grading threshold.

15. The method of claim 14, wherein the cardiac input data includes at least one electrocardiogram (ECG).

16. The method of claim 14, wherein training the at least one cardiac machine learning model comprises: receiving a plurality of cardiac training data associated with a plurality of patients; pretraining the at least one cardiac machine learning model as a function of the plurality of cardiac training data by adjusting one or more parameters within the at least one cardiac machine learning model; and 214 Attorney Docket No.1518-126PCT1retraining the at least one cardiac machine learning model as a function of the one or more parameters and a labeled subset of the plurality of cardiac training data.

17. The method of claim 16, wherein the at least one cardiac model includes a classification model that classifies a case of diastolic dysfunction under one category of a plurality of categories.

18. The method of claim 14, wherein: at least one cardiac index of the one or more cardiac indices includes an elevated left ventricular filling pressure; and the at least one grading threshold includes a grading threshold in elevated left ventricular filling pressure.

19. The method of claim 14, wherein: at least one cardiac index of the one or more cardiac indices is associated with pulmonary hypertension; and the at least one grading threshold includes a grading threshold associated with pulmonary hypertension.

20. The method of claim 14, wherein: inputting the cardiac input data into the cardiac panel comprises inputting a first plurality of time series data containing the cardiac input data; and generating the one or more cardiac indices comprises generating a second plurality of time series data containing the one or more cardiac indices.

21. The method of claim 14, wherein at least one of the one or more cardiac indices includes a cardiac index deviation.

22. The method of claim 14, wherein generating the one or more cardiac indices comprises: receiving at least one user update; and updating at least one cardiac index of the one or more cardiac indices as a function of the at least one user update.

23. The method of claim 14, wherein generating the one or more cardiac indices comprises: comparing the one or more cardiac indices to one or more cardiac baselines; calculating one or more distance metrics as a function of the comparison; and generating the one or more cardiac indices as a function of at least one distance metric of the one or more distance metrics. 215 Attorney Docket No.1518-126PCT124. The method of claim 23, wherein the method further comprises displaying, by the processor, at least one cardiac index of the one or more cardiac indices using a user interface.

25. The method of claim 24, wherein displaying the at least one cardiac index of the one or more cardiac indices further comprises generating a color-coded visualization as a function of the at least one cardiac index of the one or more cardiac indices and the at least one distance metric of the one or more distance metrics.

26. The method of claim 14, wherein the method further comprises predicting, by the processor, a projected cardiac index as a function of the cardiac input data and the at least one cardiac machine learning model.

27. An apparatus for detecting hypertension attributes in a patient time-series data, the apparatus comprising: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a patient time-series data associated with a patient, wherein the patient time-series data is captured using a measurement device; input the patient time-series data into a hypertension panel wherein the hypertension panel comprises of a plurality of hypertension models; and generate a hypertension attribute from the hypertension panel as a function of the patient time-series data and the plurality of hypertension models, wherein generating the hypertension attribute comprises: generating, using a first hypertension model, a first hypertension attribute comprising a measurement of the patient yielding a first hypertension level using the patient time-series data; and generating, using a second hypertension model, a second hypertension attribute using the patient time-series data.

28. The apparatus of claim 27, wherein the instructions further configure the at least a processor to generate a confidence score from the hypertension panel as a function of the patient time-series data and at least one of the first hypertension model and the second hypertension model. 216 Attorney Docket No.1518-126PCT129. The apparatus of claim 27, wherein the first hypertension level comprises a systolic blood pressure and a diastolic blood pressure.

30. The apparatus of claim 27, wherein the second hypertension model comprises one or more of a hypertension classifier model, a hypertension prediction model, and a hypertension correlation model.

31. The apparatus of claim 27, wherein the second hypertension model comprises a loss function.

32. The apparatus of claim 27, wherein the instructions further configure to train the second hypertension model, wherein training the second hypertension model comprises: receiving a plurality of patient time-series data examples associated with a plurality of patients; pretraining the second hypertension model in the hypertension panel as a function of the plurality of patient time-series data examples by adjusting one or more parameter attributes of the second hypertension model; and training the second hypertension model as a function of the one or more parameter attributes and an electronic health record.

33. The apparatus of claim 27, wherein the hypertension attribute comprises a hypertension deviation, wherein the hypertension deviation comprises a change in the hypertension attribute.

34. The apparatus of claim 27, wherein inputting the patient time-series data into the hypertension panel comprises selecting the second hypertension model from a plurality of hypertension models as a function of an input and a graphical user interface.

35. The apparatus of claim 27, wherein displaying the hypertension attribute comprises: comparing the hypertension attribute to a target blood pressure level; and displaying the hypertension attribute as a function of the comparison.

36. The apparatus of claim 27, wherein displaying the hypertension attribute through a graphical user interface comprises generating a visual element associated with the hypertension attribute, wherein the visual element is further associated with an event handler.

37. A method for detecting hypertension attributes in a patient time-series data, the method comprising: 217 Attorney Docket No.1518-126PCT1receiving a patient time-series data associated with a patient, wherein the patient time- series data is captured using a measurement device; inputting the patient time-series data into a hypertension panel wherein the hypertension panel comprises of a plurality of hypertension models; and generating a hypertension attribute from the hypertension panel as a function of the patient time-series data and the plurality of hypertension models, wherein generating the hypertension attribute comprises: generating, using a first hypertension model, a first hypertension attribute comprising a measurement of the patient yielding a first hypertension level using the patient time-series data; and generating, using a second hypertension model, a second hypertension attribute using the patient time-series data.

38. The method of claim 37, further comprising generating a confidence score from the hypertension panel as a function of the patient time-series data and at least one of the first hypertension model and the second hypertension model.

39. The method of claim 37, wherein the first hypertension level comprises a systolic blood pressure and a diastolic blood pressure.

40. The method of claim 37, wherein the second hypertension model comprises one or more of a hypertension classifier model, a hypertension prediction model, and a hypertension correlation model.

41. The method of claim 37, wherein the second hypertension model comprises a loss function.

42. The method of claim 37, further comprising training the second hypertension model, wherein training the second hypertension model comprises: receiving a plurality of patient time-series data examples associated with a plurality of patients; pretraining the second hypertension model in the hypertension panel as a function of the plurality of patient time-series data examples by adjusting one or more parameter attributes of the second hypertension model; and training the second hypertension model as a function of the one or more parameter attributes and an electronic health record. 218 Attorney Docket No.1518-126PCT143. The method of claim 37, wherein the hypertension attribute comprises a hypertension deviation, wherein the hypertension deviation comprises a change in the hypertension attribute.

44. The method of claim 37, wherein inputting the patient time-series data into the hypertension panel comprises selecting the second hypertension model from a plurality of hypertension models as a function of an input and a graphical user interface.

45. The method of claim 37, wherein displaying the hypertension attribute comprises: comparing the hypertension attribute to a target blood pressure level; and displaying the hypertension attribute as a function of the comparison.

46. The method of claim 37, wherein displaying the hypertension attribute through a graphical user interface comprises generating a visual element associated with the hypertension attribute, wherein the visual element is further associated with an event handler.

47. An apparatus for identifying a progression of coronary heart disease, wherein the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a subject profile associated with a subject, wherein the subject profile comprises a plurality of electrocardiogram (ECG) data; identify contextual data as a function of the subject profile; generate a set of cardiac scores as a function of the contextual data and the plurality of ECG data using a set of cardiac machine learning models; and select at least one stage of coronary heart disease from a plurality of stages of coronary heart disease as a function of the set of cardiac scores.

48. The apparatus of claim 47, wherein: the set of cardiac scores comprises at least one cardiac score associated with each stage of coronary heart disease of the plurality of stages of coronary heart disease; and the set of cardiac machine learning models comprises at least one cardiac machine learning model associated with each stage of coronary heart disease of the plurality of stages of coronary heart disease. 219 Attorney Docket No.1518-126PCT149. The apparatus of claim 48, wherein generating the set of cardiac scores comprises: iteratively training the set of cardiac machine learning models using cardiac training data, wherein the cardiac training data comprises examples of ECG data and examples of contextual data as inputs correlated to examples of cardiac scores as outputs, and wherein iteratively training the set of cardiac machine learning models comprises: classifying the cardiac training data into a plurality of cardiac training subsets associated with the plurality of stages of coronary heart disease; and iteratively training each cardiac machine learning model of the set of cardiac machine learning models using each cardiac training subset of the plurality of cardiac training subsets; and generating the set of cardiac scores using the trained set of cardiac machine learning models.

50. The apparatus of claim 47, wherein the plurality of stages of coronary heart disease comprises a plurality of ranges of total plaque volume (TPV) associated with the subject.

51. The apparatus of claim 47, wherein the plurality of stages of coronary heart disease comprises a plurality of ranges of percent arterial volume (PAV) associated with the subject.

52. The apparatus of claim 47, wherein the memory further instructs the at least a processor to generate an impact score as a function of the contextual data.

53. The apparatus of claim 52, wherein iteratively training the set of cardiac machine learning models additionally comprises calibrating each cardiac machine learning model of the set of cardiac machine learning models using the impact score.

54. The apparatus of claim 47, wherein each cardiac score of the set of cardiac scores comprises a confidence interval.

55. The apparatus of claim 54, wherein selecting the at least one stage of coronary heart disease comprises selecting the at least one stage of coronary heart disease from a plurality of stages of coronary heart disease as a function of the confidence interval of each cardiac score of the set of cardiac scores.

56. The apparatus of claim 47, wherein receiving the subject profile comprises receiving the subject profile from an electronic medical record. 220 Attorney Docket No.1518-126PCT157. A method for identifying a progression of coronary heart disease, wherein the method comprises: receiving, using at least a processor, a subject profile associated with a subject, wherein the subject profile comprises a plurality of electrocardiogram (ECG) data; identifying, using the at least a processor, contextual data as a function of the subject profile; generating, using the at least a processor, a set of cardiac scores as a function of the contextual data and the plurality of ECG data using a set of cardiac machine learning models; and selecting, using the at least a processor, at least one stage of coronary heart disease from a plurality of stages of coronary heart disease as a function of the set of cardiac scores.

58. The method of claim 57, wherein: the set of cardiac scores comprises at least one cardiac score associated with each stage of coronary heart disease of the plurality of stages of coronary heart disease; and the set of cardiac machine learning models comprises at least one cardiac machine learning model associated with each stage of coronary heart disease of the plurality of stages of coronary heart disease.

59. The method of claim 58, wherein: generating the set of cardiac scores comprises: iteratively training the set of cardiac machine learning models using cardiac training data, wherein the cardiac training data comprises examples of ECG data and examples of contextual data as inputs correlated to examples of cardiac scores as outputs; and generating the set of cardiac scores using the trained set of cardiac machine learning models; and iteratively training the set of cardiac machine learning models comprises: classifying the cardiac training data into a plurality of cardiac training subsets associated with the plurality of stages of coronary heart disease; and iteratively training each cardiac machine learning model of the set of cardiac machine learning models using each cardiac training subset of the plurality 221 Attorney Docket No.1518-126PCT1of cardiac training subsets.

60. The method of claim 57, wherein the plurality of stages of coronary heart disease comprises a plurality of ranges of total plaque volume (TPV) associated with the subject.

61. The method of claim 57, wherein the plurality of stages of coronary heart disease comprises a plurality of ranges of percent arterial volume (PAV) associated with the subject.

62. The method of claim 57, wherein the method further comprises generating, using the at least a processor, an impact score as a function of the contextual data.

63. The method of claim 62, wherein iteratively training the set of cardiac machine learning models additionally comprises calibrating each cardiac machine learning model of the set of cardiac machine learning models using the impact score.

64. The method of claim 57, wherein each cardiac score of the set of cardiac scores comprises a confidence interval.

65. The method of claim 64, wherein selecting the at least one stage of coronary heart disease comprises selecting the at least one stage of coronary heart disease from a plurality of stages of coronary heart disease as a function of the confidence interval of each cardiac score of the set of cardiac scores.

66. The method of claim 57, wherein receiving the subject profile comprises receiving the subject profile from an electronic medical record.

67. An apparatus for determining women’s health attributes in time-series data, the apparatus comprising: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive time series data associated with a female classification; input the time-series data into a women’s health panel wherein the women’s health panel comprises a plurality of women’s health models; and generate a women’s health attribute from the women’s health panel as a function of the time-series data and a women’s health model, wherein generating the women’s health attribute comprises: generating, using a first women’s health model, a first women’s health 222 Attorney Docket No.1518-126PCT1attribute; and generating, using a second women’s health model, a second women’s health attribute.

68. The apparatus of claim 67, wherein the time-series data comprises electrocardiogram data.

69. The apparatus of claim 68, wherein the apparatus further comprises a measurement device, wherein the measurement device comprises one or more transducers.

70. The apparatus of claim 68, wherein the plurality of women’s health models comprises a loss function, and the instructions further configure the at least a processor to generate a confidence score from the women’s health panel as a function of the time-series data and at least one of the first women’s health model and the second women’s health model.

71. The apparatus of claim 68, wherein the plurality of women’s health models comprises a women’s health classifier model, a women’s health prediction model, and a women’s health correlation model.

72. The apparatus of claim 68, wherein training the women’s health model comprises: receiving a plurality of time-series data examples associated with the female classification; pretraining the women’s health model in the women’s health panel as a function of the plurality of time-series data examples by adjusting one or more parameter attributes of the women’s health model; and training the women’s health model as a function of the one or more parameter attributes and a database.

73. The apparatus of claim 68, wherein the first women’s health attribute comprises a peripartum cardiomyopathy attribute, and the second women’s health attribute comprises a coronary heart disease attribute.

74. The apparatus of claim 68, wherein the women’s health attribute comprises a women’s health deviation, wherein the women’s health deviation comprises a change in the women’s health attribute.

75. The apparatus of claim 68, wherein inputting the time-series data into the women’s health panel comprises selecting the women’s health model from a plurality of women’s health models as a function of an input and a graphical user interface. 223 Attorney Docket No.1518-126PCT176. The apparatus of claim 68, wherein displaying the women’s health attribute comprises: comparing the women’s health attribute to a nominal women’s health attribute; and displaying the women’s health attribute as a function of the comparison.

77. A method for determining women’s health attributes in time-series data, the method comprising: receiving time-series data associated with a female classification; inputting the time-series data into a women’s health panel wherein the women’s health panel comprises of a plurality of women’s health models; and generating a women’s health attribute from the women’s health panel as a function of the time-series data and a women’s health model, wherein generating the women’s health attribute comprises: generating, using a first women’s health model, a first women’s health attribute; and generating, using a second women’s health model, a second women’s health attribute comprising.

78. The method of claim 77, wherein the time-series data comprises electrocardiogram data.

79. The method of claim 78, wherein the method further comprises a measurement device, wherein the measurement device comprises one or more transducers.

80. The method of claim 78, wherein the plurality of women’s health models comprises a loss function, and the method further comprises generating a confidence score from the women’s health panel as a function of the time-series data and at least one of the first women’s health model and the second women’s health model.

81. The method of claim 78, wherein the plurality of women’s health models comprises a women’s health classifier model, a women’s health prediction model, and a women’s health correlation model.

82. The method of claim 78, wherein training the women’s health model comprises: receiving a plurality of time-series data examples associated with the female classification; pretraining the women’s health model in the women’s health panel as a function of the plurality of time-series data examples by adjusting one or more parameter attributes of the women’s health model; and 224 Attorney Docket No.1518-126PCT1training the women’s health model as a function of the one or more parameter attributes and a database.

83. The method of claim 78, wherein the first women’s health attribute comprises a peripartum cardiomyopathy level, and the second women’s health attribute comprises a coronary heart disease level.

84. The method of claim 78, wherein the women’s health attribute comprises a women’s health attribute deviation, wherein the women’s health attribute deviation comprises a change in the women’s health attribute.

85. The method of claim 78, wherein inputting the time-series data into the women’s health panel comprises selecting the women’s health model from a plurality of women’s health models as a function of an input and a graphical user interface.

86. The method of claim 78, wherein displaying the women’s health attribute comprises: comparing the women’s health attribute to a nominal women’s health attribute; and displaying the women’s health attribute as a function of the comparison.

87. An apparatus for generating a preoperative data structure using a pre-operative panel, the apparatus comprising: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive subject data, wherein the subject data comprises electrocardiogram (ECG) data; generate a plurality of panel outputs as a function of the subject data using a pre- operative panel machine-learning module; wherein the pre-operative panel machine-learning module comprises a plurality of panel machine-learning models, wherein each of the plurality of panel machine-learning models is configured to generate one panel output for one panel focus as a function of the subject data; wherein generating the plurality of panel outputs comprises: generating a plurality of sets of panel training data, wherein the plurality of sets of panel training data comprises correlations between exemplary subject data, exemplary panel focuses and exemplary 225 Attorney Docket No.1518-126PCT1panel outputs; training each of the plurality of panel machine-learning models using each of the plurality of sets of panel training data; and generating the plurality of panel outputs using the plurality of trained panel machine-learning models; and generate a pre-operative data structure as a function of the plurality of panel outputs.

88. The apparatus of claim 87, wherein generating the plurality of panel outputs comprises: determining at least an ECG feature as a function of the ECG data; and determining the plurality of panel outputs as a function of the ECG feature.

89. The apparatus of claim 88, wherein determining the at least an ECG feature further comprises: generating ECG feature training data, wherein the ECG feature training data comprises correlations between exemplary ECG data and exemplary ECG features; training an ECG feature machine-learning model using the ECG feature training data; and determining the at least an ECG feature using the trained ECG feature machine-learning model.

90. The apparatus of claim 87, wherein the plurality of panel machine-learning models comprises a first panel machine-learning model comprising a first panel focus related to coronary heart disease, wherein the first panel machine-learning model is configured to generate a first panel output related to the coronary heart disease as a function of the subject data.

91. The apparatus of claim 87, wherein the plurality of panel machine-learning models comprises a second panel machine-learning model comprising a second panel focus related to pulmonary hypertension, wherein the second panel machine-learning model is configured to generate a second panel output related to the pulmonary hypertension as a function of the subject data.

92. The apparatus of claim 87, wherein the plurality of panel machine-learning models comprises a third panel machine-learning model comprising a third panel focus related to atrial fibrillation, wherein the third panel machine-learning model is configured to generate a third panel output related to the atrial fibrillation as a function of the subject 226 Attorney Docket No.1518-126PCT1data.

93. The apparatus of claim 87, wherein the plurality of panel machine-learning models comprises a fourth panel machine-learning model comprising a fourth panel focus related to ejection fraction, wherein the fourth panel machine-learning model is configured to generate a fourth panel output related to the ejection fraction as a function of the subject data.

94. The apparatus of claim 87, wherein the memory contains instructions further configuring the at least a processor to: generate cohort training data, wherein the cohort training data comprises correlations between exemplary subject data and exemplary subject cohorts; train a cohort classifier using the cohort training data; and classify the subject data to one or more subject cohorts using the trained cohort classifier.

95. The apparatus of claim 94, wherein the memory contains instructions further configuring the at least a processor to update the panel training data as a function of an output of the cohort classifier.

96. The apparatus of claim 87, wherein the plurality of panel outputs comprises a pre- operative optimization output.

97. A method for generating a preoperative data structure using a pre-operative panel, the method comprising: receiving, using at least a processor, subject data, wherein the subject data comprises electrocardiogram (ECG) data; generating, using the at least a processor, a plurality of panel outputs as a function of the subject data using a pre-operative panel machine-learning module; wherein the pre-operative panel machine-learning module comprises a plurality of panel machine-learning models, wherein each of the plurality of panel machine-learning models is configured to generate one panel output for one panel focus as a function of the subject data; wherein generating the plurality of panel outputs comprises: generating a plurality of sets of panel training data, wherein the plurality of sets of panel training data comprises correlations between exemplary subject data, exemplary panel focuses and exemplary panel outputs; 227 Attorney Docket No.1518-126PCT1training each of the plurality of panel machine-learning models using each of the plurality of sets of panel training data; and generating the plurality of panel outputs using the plurality of trained panel machine-learning models; and generating, using the at least a processor, a pre-operative data structure as a function of the plurality of panel outputs.

98. The method of claim 97, wherein generating the plurality of panel outputs comprises: determining, using the at least a processor, at least an ECG feature as a function of the ECG data; and determining, using the at least a processor, the plurality of panel outputs as a function of the ECG feature.

99. The method of claim 98, wherein determining the at least an ECG feature further comprises: generating, using the at least a processor, ECG feature training data, wherein the ECG feature training data comprises correlations between exemplary ECG data and exemplary ECG features; training, using the at least a processor, an ECG feature machine-learning model using the ECG feature training data; and determining, using the at least a processor, the at least an ECG feature using the trained ECG feature machine-learning model.

100. The method of claim 97, wherein the plurality of panel machine-learning models comprises a first panel machine-learning model comprising a first panel focus related to coronary heart disease, wherein the first panel machine-learning model is configured to generate a first panel output related to the coronary heart disease as a function of the subject data.

101. The method of claim 97, wherein the plurality of panel machine-learning models comprises a second panel machine-learning model comprising a second panel focus related to pulmonary hypertension, wherein the second panel machine-learning model is configured to generate a second panel output related to the pulmonary hypertension as a function of the subject data.

102. The method of claim 97, wherein the plurality of panel machine-learning models 228 Attorney Docket No.1518-126PCT1comprises a third panel machine-learning model comprising a third panel focus related to atrial fibrillation, wherein the third panel machine-learning model is configured to generate a third panel output related to the atrial fibrillation as a function of the subject data.

103. The method of claim 97, wherein the plurality of panel machine-learning models comprises a fourth panel machine-learning model comprising a fourth panel focus related to ejection fraction, wherein the fourth panel machine-learning model is configured to generate a fourth panel output related to the ejection fraction as a function of the subject data.

104. The method of claim 97, further comprising: generating, using the at least a processor, cohort training data, wherein the cohort training data comprises correlations between exemplary subject data and exemplary subject cohorts; training, using the at least a processor, a cohort classifier using the cohort training data; and classifying, using the at least a processor, the subject data to one or more subject cohorts using the trained cohort classifier.

105. The method of claim 104, further comprising: updating, using the at least a processor, the panel training data as a function of an output of the cohort classifier.

106. The method of claim 97, wherein the plurality of panel outputs comprises a pre-operative optimization output. 229 Attorney Docket No.1518-126PCT1

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