Apparatus and method for generating diagnostic label

Abstract

To provide an apparatus and a method for generating a diagnostic label.SOLUTION: An apparatus 100 for generating a diagnostic label includes at least one processor and a memory capable of being communicatively connected to the at least one processor. The memory instructs the processor to receive a plurality of electrocardiogram signals and a plurality of electronic health records from a user. The memory instructs the processor to generate a plurality of structured electronic health records using the plurality of electronic health records, to generate a plurality of representations as a function of the plurality of electrocardiogram signals and the plurality of structured electronic health records using a representation machine learning model, to generate a diagnostic label as a function of the plurality of representations, and to display the diagnostic label using a display device.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates generally to the field of artificial intelligence, and more particularly to an apparatus and method for generating diagnostic labels. [Background technology]

[0002] In modern healthcare, the collection and storage of patient health data has evolved significantly. Electronic Health Record (EHR) systems have been widely adopted to organize structured patient data including demographic information, diagnoses, medications, and test results. However, effectively utilizing the wealth of information contained within the EHR along with textual data such as physician notes, reports, and patient narratives and correlating it with diagnostic ECG signals remains a challenge. Summary of the Invention

[0003] In one aspect, an apparatus for generating a diagnostic label is disclosed. The apparatus includes at least one processor and a memory communicatively connected to the at least one processor. The memory instructs the processor to receive a plurality of electrocardiogram signals and a plurality of electronic health records from a user. The memory instructs the processor to generate a plurality of structured electronic health records using the plurality of electronic health records. The memory instructs the processor to generate a plurality of representations as a function of the plurality of electrocardiogram signals and the plurality of structured electronic health records using a representation machine learning model. Generating the plurality of representations includes training the representation machine learning model using the representation training data. Generating the plurality of representations also includes generating a plurality of representations as a function of the plurality of electrocardiogram signals and the plurality of structured electronic health records using the trained representation machine learning model. The memory instructs the processor to generate a diagnostic label as a function of the plurality of representations. The memory instructs the processor to display the diagnostic label using a display device.

[0004] In another aspect, a method for generating a diagnostic label is disclosed. The method includes receiving, using at least one processor, a plurality of electrocardiogram signals from a user. The method includes receiving, using at least one processor, a plurality of electronic health records from the user, the plurality of electronic health records including a plurality of metadata. The method includes generating, using at least one processor, a plurality of structured electronic health records using the plurality of electronic health records. The method includes generating, using at least one processor, a plurality of representations as a function of the plurality of electrocardiogram signals and the plurality of structured electronic health records using a representation machine learning model. Generating the plurality of representations includes training the representation machine learning model using the representation training data. Generating the plurality of representations also includes generating, using the trained representation machine learning model, a plurality of representations as a function of the plurality of electrocardiogram signals and the plurality of structured electronic health records. The method includes generating, using at least one processor, a diagnostic label as a function of the plurality of representations. The method includes displaying, using at least one processor, the diagnostic label using a display device.

[0005] These and other aspects and features of the non-limiting embodiments of the present invention will become apparent to those of ordinary skill in the art upon review of the following description of specific non-limiting embodiments of the present invention in conjunction with the accompanying drawings.

[0006] For the purpose of illustrating the invention, the drawings show aspects of one or more embodiments of the invention, it being understood, however, that the invention is not limited to the precise arrangements and instrumentalities shown in the drawings. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a block diagram of an exemplary embodiment of an apparatus for generating a diagnostic label. [Diagram 2] FIG. 1 is a block diagram of an example machine learning process. [Diagram 3]FIG. 2 is a block diagram of an exemplary embodiment of an electrocardiogram database. [Figure 4] FIG. 1 is a diagram of an exemplary embodiment of a neural network. [Diagram 5] FIG. 2 is a diagram of an exemplary embodiment of a node of a neural network. [Figure 6A] 1 illustrates an example embodiment of a number of models used to generate each of a number of representations. [Figure 6B] 1 illustrates an example embodiment of a number of models used to generate each of a number of representations. [Figure 6C] 1 illustrates an example embodiment of a number of models used to generate each of a number of representations. [Figure 6D] 1 illustrates an example embodiment of a number of models used to generate each of a number of representations. [Figure 6E] 1 illustrates an example embodiment of a number of models used to generate each of a number of representations. [Figure 6F] 1 illustrates an example embodiment of a number of models used to generate each of a number of representations. [Figure 7] FIG. 1 is a flow diagram of an exemplary method for generating a diagnostic label. [Figure 8] FIG. 1 is a block diagram of a computing system that may be used to implement any one or more of the methodologies disclosed herein and any one or more portions thereof. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] The drawings are not necessarily to scale and may be illustrated by phantom lines, diagrammatic representations, and partial views. In some cases, details that are not necessary for understanding the embodiments or that make other details difficult to perceive may be omitted.

[0009] At a high level, aspects of the present disclosure relate to an apparatus and method for generating a diagnostic label. The apparatus includes at least one processor and a memory communicatively connected to the at least one processor. The memory instructs the processor to receive a plurality of electrocardiogram signals and a plurality of electronic health records from a user. The memory instructs the processor to generate a plurality of structured electronic health records using the plurality of electronic health records. The memory instructs the processor to generate a plurality of representations as a function of the plurality of electrocardiogram signals and the plurality of structured electronic health records using a representational machine learning model. The memory instructs the processor to generate a diagnostic label as a function of the plurality of representations. The memory instructs the processor to display the diagnostic label using a display device. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples.

[0010] Electronic health records (EHRs) provide a rich source of medical data across various modalities such as electrocardiograms (ECGs), patient notes (text), and structured EHRs (SEQs), which capture information on disease diagnoses, treatments, and medication prescriptions in the form of International Classification of Diseases (ICD) codes. In some cases, the device 100 can combine multimodal data such as ECGs, structured EHRs, and text modalities to generate multiple representations of the combination of data. The multiple representations can be used for various downstream tasks. The multiple representations can be compared to various versions of the multiple representations. Comparisons can be made using baseline models, i.e., supervised learning models trained from scratch with random initialization, currently available self-supervised learning models trained only on ECGs, and supervised massive multi-task learning models trained to perform both classification and regression tasks on a large number of disease and laboratory test measurements. The machine learning models can be trained from a database containing a large number of ECGs, and the models can be evaluated on downstream tasks such as linear classification, including prediction of various cardiac conditions, demonstrating that the models presented in this study show significant improvement over all baseline models.

[0011] Referring now to FIG. 1, an exemplary embodiment of an apparatus 100 for generating a diagnostic label is shown. The apparatus 100 includes a processor 104. The processor 104 may include any computing device described herein, including, but not limited to, a microcontroller, a microprocessor, a digital signal processor (DSP), and / or a system on a chip (SoC) described herein. The computing device may be included in and / or communicate with a mobile device, including a mobile device such as a mobile phone or smartphone. The processor 104 may include a single computing device operating independently, or may include two or more computing devices operating in concert, in parallel, sequentially, etc. Two or more computing devices may be included together in a single computing device or two or more computing devices. The processor 104 may interface or communicate with one or more additional devices, as described in more detail below, via a network interface device. The network interface device may be utilized to connect the processor 104 to one or more of the various networks and to one or more devices. Examples of network interface devices 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 networks 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 combination thereof. A network can use wired and / or wireless communication modes. In general, any network topology can be used.Information (e.g., data, software, etc.) can be communicated between computers and / or computing devices. The processor 104 can include, for example, but not limited to, a computing device or cluster of computing devices at a first location and a second computing device or cluster of computing devices at a second location. The processor 104 can include one or more computing devices dedicated to data storage, security, traffic distribution for load balancing, and the like. The processor 104 can distribute one or more computing tasks, as described below, across multiple computing devices, which can operate in parallel, serially, redundantly, or any other manner used to distribute tasks or memory between computing devices. The processor 104 may be implemented using a “shared nothing” architecture, where data is cached at the workers, which in one embodiment can enable scalability of the apparatus 100 and / or computing devices.

[0012] With continued reference to FIG. 1, the processor 104 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 at any iterative degree. For example, the processor 104 may be configured to repeatedly perform a single step or sequence until a desired or commanded result is achieved. The iterations of a step or sequence of steps may be performed iteratively and / or recursively using the output of a previous iteration as input to a subsequent iteration, and the inputs and / or outputs of the iterations may be aggregated to produce an aggregated result, a decrease or decrement of one or more variables, such as a global variable, and / or a division of a larger processing task into a set of smaller processing tasks that are iteratively addressed. The processor 104 may perform any step or sequence of steps described in this disclosure in parallel, such as performing a step two or more times simultaneously and / or substantially simultaneously using two or more parallel threads, processor cores, or the like. The division of tasks among parallel threads and / or processes may be performed according to any protocol suitable for dividing tasks among iterations. Those skilled in the art will recognize upon reviewing this disclosure in its entirety the various ways in which processes, sequences of processes, processing tasks, and / or data can be subdivided, shared, or otherwise processed using iterative, recursive, and / or parallel processing.

[0013] With continued reference to FIG. 1, the device 100 includes a memory. The memory is communicatively connected to the processor 104. The memory can include instructions that configure the processor 104 to perform the tasks disclosed in this disclosure. As used in this disclosure, "communicatively connected" means connected by a connection, attachment, or link between two or more related items that allows for the receipt and / or transmission of information between them. For example, but not limited to, the connection can be wired or wireless, direct or indirect, allowing for the receipt and / or transmission of data and / or signals between two or more components, circuits, devices, systems, apparatus, and the like. The data and / or signals therebetween can include, but are not limited to, among others, electrical, electromagnetic, magnetic, video, audio, radio, and microwave data and / or signals, combinations thereof, and the like. The communication connection can be achieved, for example, but not limited to, via wired or wireless electronic, digital, or analog communication, directly or through one or more intervening devices or components. Additionally, a communicative connection may include electrically coupling or connecting at least one output of one device, component, or circuit to at least one input of another device, component, or circuit, for example, but not limited to, via a bus or other facility for intercommunication between elements of a computing device. A communicative connection may also include an indirect connection, for example, but not limited to, via wireless connections, wireless communication, low power wide area networks, optical communication, magnetic coupling, capacitive coupling, or optical coupling, and the like. In some cases, the term "communicatively coupled" may be used in place of communicatively connected in this disclosure.

[0014] With continued reference to FIG. 1, the processor 104 is configured to receive a number of electrocardiogram (ECG) signals 108 from a user. As used in this disclosure, an "electrocardiogram signal" is a signal representative of the electrical activity of the heart. The ECG signal 108 may be composed of several separate waves and intervals, each representing a different phase of the cardiac cycle. These waves may include P waves, QRS complexes, T waves, U waves, and the like. The P waves may represent depolarization (contraction) of the atria as the electrical impulse spreads through the atria. The QRS complexes may represent depolarization (contraction) of the ventricles as the electrical impulse spreads through the ventricles. The QRS complexes may include three waves: Q waves, R waves, and S waves. The T waves may represent ventricular repolarization (recovery) as the ventricles prepare for the next contraction. The U waves may be present after the T waves and represent repolarization of the Purkinje fibers. The intervals between these waves provide information regarding the duration and regularity of the various phases of the cardiac cycle. The ECG signal can aid in the diagnosis of various cardiac conditions such as arrhythmias, myocardial infarction (heart attack), conduction abnormalities, and electrolyte imbalances. In one embodiment, each sensor can generate an individual ECG signal 108.

[0015] Continuing with reference to FIG. 1, the electrocardiogram signals 108 can be generated using at least one sensor. As used in this disclosure, a "sensor" is a device configured to detect an input and / or phenomenon and transmit information related to the detection. The sensor can detect data. The data detected by the sensor can include, but is not limited to, the electrocardiogram signal 108, heart rate, blood pressure, electrical signals related to the heart, and the like. In one or more embodiments, but is not limited to, the sensor can include sensors. In one or more embodiments, but is not limited to, the sensor can include one or more electrodes, and the like. Electrodes used in electrocardiograms (ECGs) are small sensors or conductive patches that are placed at specific locations on the body to detect and record electrical signals generated by the heart. The sensor acts as an interface between the body and the ECG equipment, allowing the measurement and recording of the electrical activity of the heart. The sensors can include the ten electrodes used in a standard 12-lead ECG, placed at specific locations on the patient's chest and extremities. These electrodes are typically made of conductive materials such as metal or carbon and are connected to leads that transmit electrical signals to the ECG equipment for recording. Proper electrode placement can be important to ensure accurate signal detection and recording. The number of electrodes used by the ECG equipment can depend on the particular equipment in use and can vary from a single electrode on a wearable device to 12 or more electrodes, or any number in between.

[0016] Continuing to refer to FIG. 1, multiple sensors may be placed on each limb, with at least one sensor on each arm and leg. 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, multiple sensors may be placed on various parts of the patient's torso and chest. For example, sensor V1 may be placed in the fourth intercostal space on both right sternal edges, and sensor V2 may be in the fourth intercostal space on both left sternal edges. Sensor V3 may be placed between sensors V2 and V4, midway between their locations. Sensor V4 may be placed in the fifth intercostal space on the midclavicular line. Sensor V5 may be placed at the same level as sensor V4, but horizontally on the anterior axillary line. Sensor V6 may be placed at the same level as V4 and V5, but horizontally on the mid-axillary line.

[0017] With continued reference to FIG. 1 , the multiple sensors can include an extended unipolar sensor. These sensors can be labeled aVR, aVL, and aVF. These sensors can be derived from the limb sensors and provide additional information about the electrical activity of the heart. These leads are calculated using a particular combination of limb leads and serve to evaluate electrical vectors in different orientations. For example, aVR can be derived from sensor II and sensor III. In another example, aVL can be derived from sensor I and sensor III. Additionally, aVF can be derived from lead I and lead II. The combination of limb sensors, precordial sensors, and extended unipolar sensors allows for a comprehensive evaluation of the electrical activity of the heart in three dimensions.

[0018] With continued reference to FIG. 1 , the processor 104 is configured to receive a plurality of electronic health records (EHR) 112. As used in this disclosure, an “electronic health record” is a data structure that includes a collection of health data associated with a user. As used in this disclosure, “health data” refers to a collection of information related to a patient's health and health care. Health data can include elements of data related to treatment records, medical history, test results, radiology reports, medical records, clinical notes, and the like. An electronic health record (EHR) 112 is a digital version of a patient's medical information that is stored and managed in a computerized system. It is a comprehensive longitudinal collection of a patient's health-related data, including medical history, diagnoses, medications, treatment plans, test results, and other relevant health information. The EHR 112 includes a wide range of patient information, including personal demographics, medical history, allergies, vaccinations, medications, test results, imaging reports, surgical procedures, and progress notes. This comprehensive data provides a complete overview of a patient's health and facilitates informed decision-making. The EHR 112 may include the patient's past and current medical conditions, surgeries, allergies, immunization records, medications, and any significant health events. The EHR 112 may further include a wealth of information regarding the patient's health background. This may include previous diagnoses, medical tests, medical imaging, and the like. The EHR may include documentation, findings, evaluations, and treatment plans from medical professionals. This may include progress notes, discharge summaries, and other relevant clinical documentation. The EHR may include information regarding prescribed medications, including dosage, frequency, symptoms, effects, and duration. The EHR may include test results, which may include laboratory test results, radiology reports, medical imaging reports, and other diagnostic imaging findings.

[0019] With continued reference to FIG. 1 , the EHR 112 may be received by the processor 104 via user input. For example, but not by way of limitation, a user or third party may manually input the EHR 112 using a graphical user interface of the processor 104 or a remote device, such as, for example, a smartphone or laptop. The EHR 112 may further be generated through answers to a series of questions. In a non-limiting embodiment, the user may be prompted to input certain information or may complete a questionnaire. In one embodiment, the graphical user interface may display a series of questions to prompt the user for information regarding the EHR 112. The EHR 112 may be transmitted to the processor 104, for example, via wired or wireless communication, as previously described in this disclosure.

[0020] With continued reference to FIG. 1, the EHRs 112 can include metadata 116. As used in this disclosure, "metadata" refers to descriptive information or attributes that provide context, structure, and meaning to data. Metadata 116 is essentially data about data. Metadata 116 helps understand and manage various aspects of data, such as its origin, content, format, quality, and use. It plays a key role in effectively organizing, searching, and interpreting data. Metadata 116 can include descriptive metadata, structural metadata, administrative metadata, technical metadata, provenance metadata, usage metadata, and the like. Metadata 116 can be organized and managed via 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, the metadata 116 may be associated with textual data or image data, both of which are described in more detail herein below. In some cases, the metadata 116 may include data associated with the patient's health and the patient's medical records. The metadata 116 includes patient-specific information such as a unique identifier (e.g., medical record number, national identification number), patient demographics (name, date of birth, gender), contact details, and emergency contact information. These identifiers help link and identify the EHR 112 of an individual patient. Each entry or update in the EHR 112 is typically accompanied by a date and timestamp. This metadata 116 captures when an event or documentation took place, allowing the healthcare provider to track the chronology of the patient's encounters, treatments, test results, and other relevant information. The metadata 116 may include details about the healthcare professional or user who created or updated the entry 112 in a particular EHR. This information may include the author's name, credentials, role, and department. It helps ensure accountability, audit trails, and data integrity.The metadata 116 can 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 an external healthcare organization, or captured via a patient-generated source (e.g., wearables, patient-reported data). The metadata 116 can include information about the access permissions, user roles, and security settings associated with the EHR 112. This metadata 116 may be used to enforce privacy and security protocols, ensuring that only authorized individuals can view, modify, or access certain parts of the EHR. The metadata 116 can include notes, comments, or observations made by the medical professional. These annotations can highlight particular features, anomalies, or noteworthy aspects of the EHR 112. The date and time that a slide was prepared, analyzed, or labeled can be associated as metadata.

[0021] Continuing with reference to FIG. 1, the EHR 112 may include a plurality of multimodal data associated with a user. As used in this disclosure, "multimodal data" is data that includes multiple modality data. The modalities of the data may include images, text, audio, documents, electronic health records, sensor data, and the like. The multimodal data may include text data. As used in this disclosure, "text data" is a collection of data that consists of text-based information. Text data may include any written information such as documents, emails, notes, handwriting, chat conversations, and the like. Examples of text data may include documents, titles, sentences, paragraphs, free text fields, transcriptions, prognostic labels, and the like. The text data may include data from a plurality of digital or handwritten notes. The notes may be written by a medical professional. The notes may describe the patient's condition. The text data may be associated with an electronic health record (EHR). The text data may refer to written or typed information that is recorded and stored as part of the patient's health record in digital form. It includes a wide range of textual information that provides details about a patient's medical history, diagnosis, treatment, procedure, medication, findings, clinical notes, and other relevant healthcare information. Multimodal data can include image data. As used in this disclosure, "image data" is a collection of data consisting of data associated with multiple images. Image data encompasses visual representations captured via a camera or generated via medical imaging, graphs, microscopes, or other image capture systems. Image data associated with an electronic health record (EHR) refers to visual information linked or integrated with a patient's health record. It includes medical images such as x-rays, CT scans, MRI scans, ultrasound images, endoscopic images, pathology slides, and other types of diagnostic or clinical images.

[0022] With further reference to FIG. 1, the processor 104 may use optical character recognition or optical character reading (OCR), including automatic conversion of written (e.g., typed, handwritten, or printed) images to machine-encoded text. Images of written or printed text may be included in image data, text data, or any other data mentioned throughout this disclosure. In some cases, the recognition of at least one keyword from the image component may include one or more processes, including, but not limited to, optical character recognition (OCR), optical word recognition, intelligent character recognition, intelligent word recognition, and the like. In some cases, OCR may recognize written text on a script or character-by-character basis. In some cases, optical word recognition may recognize written text on a word-by-word basis, for example, for languages ​​that use spaces as a separator. In some cases, intelligent character recognition (ICR) may recognize written text on a script or character-by-character basis, for example, by using machine learning processes. In some cases, intelligent character recognition (IWR) may recognize written text on a word-by-word basis, for example, by using machine learning processes.

[0023] Still referring to FIG. 1, in some cases, OCR can be an "offline" process that analyzes static documents or image frames. In some cases, handwriting analysis can be used as input to handwriting recognition. For example, rather than simply using the shapes of characters and words, the technique may capture the order in which lines are drawn, the direction, pen down and lift patterns, and other movements. This additional information can make handwriting recognition more accurate. In some cases, the technique is also referred to as "online," dynamic, real-time, and intelligent character recognition.

[0024] With further reference to FIG. 1, in some cases, the OCR process may use pre-processing of the image components. Pre-processing processes may include, but are not limited to, deskewing, despeckling, binarization, line segment removal, layout analysis or "region segmentation", line and word detection, script recognition, character separation or "segmentation", and normalization. In some cases, the deskewing process may include applying a transformation (e.g., homography or affine transformation) to the image components to align the text. In some cases, the despeckling process may include removing positive and negative spots and / or smoothing edges. In some cases, the binarization process may include converting the image from color or grayscale to black and white (i.e., a binary image). Binarization may be implemented as a simple method 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 the OCR algorithm used only works on binary images. In some cases, the line segment removal process may include removing non-character or non-character images (e.g., boxes and lines). In some cases, a layout analysis or "region segmentation" process can identify columns, paragraphs, titles, etc. as separate blocks. In some cases, a line and word detection process can establish baselines for words and character shapes and separate words as needed. In some cases, a script recognition process can identify scripts that allow for selecting an appropriate OCR algorithm, for example in a multi-language document. In some cases, a character separation or "segmentation" process can separate signal characters, for example a character-based OCR algorithm. In some cases, a normalization process can normalize the aspect ratio and / or scale of image components.

[0025] Still referring to FIG. 1 , in some embodiments, the OCR process includes an OCR algorithm. Exemplary OCR algorithms include a matrix matching process and / or a feature extraction process. Matrix matching may include comparing the image to a stored character 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 depend on the input character being properly separated from the rest of the image components. Matrix matching may also depend on the stored character being of a similar font and scale as the input character. Matrix matching may work best with typewritten text.

[0026] With further reference to FIG. 1, in some embodiments, the OCR process may include a feature extraction algorithm. In some cases, feature extraction may decompose a glyph into features. Exemplary, non-limiting features may include corners, edges, lines, closed loops, line directions, line intersections, and the like. In some cases, feature extraction may reduce the dimensionality of the representation, making the recognition process computationally more efficient. In some cases, the extracted features may be compared to an abstract vector-like representation of the character, which may be reduced to one or more glyph prototypes. Common techniques of feature detection in computer vision are applicable to this type of OCR. In some embodiments, a machine learning process such as a nearest neighbor classifier (e.g., k-nearest neighbors) may be used to compare image features to stored glyph features and select the closest match. The OCR may use any of the machine learning processes described in this disclosure, such as those described with reference to FIGS. 2 and 4-5. Exemplary, non-limiting OCR software includes Cuneiform and Tesseract. Cuneiform is a multilingual, open-source optical character recognition system originally developed by Cognitive Technologies in Moscow, Russia. Tesseract is a free OCR software originally developed by Hewlett-Packard of Palo Alto, California, USA.

[0027] Still referring to FIG. 1, in some cases, the OCR can use a two-pass approach to character recognition. The second pass can include adaptive recognition, using character shapes that were reliably recognized on the first pass to better recognize the remaining characters on the second pass. In some cases, the two-pass approach can be advantageous for abnormal fonts or low-quality image components where the visual language content may be distorted. Another exemplary OCR software tool includes OCRopus. Development of OCRopus is led by the German Research Centre for Artificial Intelligence in Kaiserslautern, Germany. In some cases, the OCR software can use neural networks, such as those taught with reference to FIGS. 2 and 4-5.

[0028] With further reference to FIG. 1, in some cases, OCR may include post-processing. For example, OCR accuracy may be increased if the output is constrained by a lexicon. The lexicon may include a list or set of words that are allowed to occur in the document. In some cases, the lexicon may include, for example, all words in English, or more technical vocabulary for a particular domain. In some cases, the output stream may be a plain text stream or character file. In some cases, the OCR process may preserve the original layout of the visual linguistic content. In some cases, neighborhood analysis may utilize co-occurrence frequency to correct errors by noting that certain words are often found together. For example, "Washington DC" is generally much more common in English than "Washington DOC". In some cases, the OCR process may utilize a priori knowledge of the grammar of the language being recognized. For example, grammar rules may be used to help determine whether a word is likely to be a verb or a noun. Distance conceptualizations may be used for recognition and classification. For example, the Levenshtein distance algorithm may be used in OCR post-processing to further optimize the results.

[0029] With continued reference to FIG. 1 , the processor 104 can be configured to generate a structured electronic health record 112 (structured EHR or sEHR) as a function of the electronic health record 120 (EHR). As used in this disclosure, a “structured electronic health record” is a type of electronic health record in which data is organized in a structured format using standardized terminology and data models. The structured EHR 120 can be represented by a number of diagnostic codes associated with the health data of the EHR 112, which will be described in more detail herein below. In the structured EHR 120, data is captured and stored using predefined data fields, codes, and standardized terminology. This structured approach allows for consistency, interoperability, and ease of data retrieval and analysis. The structured EHR 120 can follow a consistent and uniform structure, allowing for easy data retrieval and analysis. They typically include areas or specific sections of patient demographics, medical history, medication details, allergies, vital signs, test results, diagnosis, treatment, treatment plan, and the like. The structured EHR 120 may follow standardized data models and formats such as HL7 (Health Level 7) or CDA (Clinical Document Architecture). The structured EHR 120 may use standardized medical terminologies such as SNOMED CT (Systematized Nomenclature of Medicine-Clinical Terms) and LOINC (Logical Observation Identifiers Name and Codes) to ensure uniformity of data representation, facilitating better communication, data analysis, and decision support.

[0030] With continued reference to FIG. 1 , the processor 104 can generate the structured electronic health record 120 by classifying the health data into a plurality of diagnostic codes. As used in this disclosure, a "diagnostic code" is a plurality of code classifications of diseases, injuries, and other health conditions. The diagnostic codes can include numeric or alphanumeric codes that represent various parts of the user's medical history. In one embodiment, the diagnostic codes can include variations or combinations of ICD diagnosis codes, ICD procedure codes, and medication prescriptions used in the International Classification of Diseases (ICD) to classify and categorize medical diagnoses. The health data may be assigned one or more diagnostic codes as a function of the content of the health data. Each diagnostic code can include a corresponding medical code, a time code, and a type of medical code. The time code within the diagnostic code can represent the medical condition of the user within a given period of time. For example, a time code associated with a user may be generated each week according to the health conditions and / or treatments the user experienced during that week. The medical codes may be divided into 10 or more categories. For example, medical code categories may represent special tokens, diseases, symptoms, categories related to pregnancy / congenital external / other, cardiac CPT procedure non-cardiac CPT procedure PCS codes, HCPCS codes, medication generic names, and the like. Diagnosis codes may follow a hierarchical structure in which codes are organized into chapters, sections, and categories to facilitate easy navigation and identification of specific conditions. Diagnosis codes may utilize an alphanumeric format, typically consisting of three to seven characters, providing a range of specificity and clinical detail. Diagnosis codes may provide granularity and specificity, allowing for precise description of diseases and health conditions. Diagnosis codes may be received from database 300.

[0031] With continued reference to FIG. 1 , the processor 104 may generate the structured electronic health record 120 using a structured classifier. As used in this disclosure, a “structured classifier” is a classifier configured to generate the structured electronic health record 120. The structured classifier may correspond to a classifier described below in FIG. 2. Inputs to the structured classifier may include the EHR 112, health data, metadata 116, diagnostic codes, examples of the structured electronic health record 120, and the like. Outputs to the structured classifier may include the structured electronic health record 120. The structured classifier may be configured to classify the plurality of health data into diagnostic codes. The structured training data may include a plurality of data entries including a plurality of inputs that correlate to a plurality of outputs for training the processor by a machine learning process. In one embodiment, the structured training data may include a plurality of health data that correlate to the diagnostic codes. Additionally, the structured training data may include health data and diagnostic codes that correlate to the examples of the structured electronic health record 120. The structured training data may be received from a database 300. The structural training data may include information about the EHR 112, health data, metadata 116, diagnostic codes, examples of structured electronic health records 120, and the like. In one embodiment, the structural training data may be iteratively updated as a function of the input and output results of past structural classifiers or any other classifiers mentioned throughout this disclosure. Classifiers may include, but are not limited to, linear classifiers such as logistic regression and / or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbor classifiers, support vector machines, least squares support vector machines, Fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers.

[0032] Continuing with reference to FIG. 1 , the processor 104 is configured to generate the multiple representations 124 as a function of the multiple electrocardiogram signals 108 and the structured electronic health record 120. As used in this disclosure, “multiple representations” refers to a relationship or connection established between two or more elements of data. The multiple representations 124 may be representations of multiple pieces of data associated with the electrocardiogram signals 108 and the structured electronic health record 120. The multiple representations 124 may include a parametric map that takes high-dimensional raw data and abstracts it into a low-dimensional feature vector that ideally encapsulates essential information. The multiple representations 124 may include concatenating or integrating the ECG signals 108 with descriptive or explanatory multimodal data to provide additional context, enhance understanding, and convey relevant information. In one embodiment, the multiple representations 124 may include a relationship or connection between the ECG signals 108 and the EHR 112. In another embodiment, the multiple representations 124 may include a relationship or connection between the ECG signals 108 and the structured EHR 120. Additionally, the plurality of representations 124 may include relationships or connections between the ECG signal 108, the structured EHR 120, and the textual data. The plurality of representations 124 may include a longitudinal record of the patient's health history. By linking the ECG signal 108 to the plurality of multi-modal data, the processor 104 may track and analyze the patient's cardiac health over time. This longitudinal view may allow for the identification of trends, changes, or patterns in the ECG signal to aid in the detection of cardiac abnormalities or changes in the patient's cardiovascular health.

[0033] Continuing with reference to FIG. 1, the plurality of representations 124 may be represented in vector form. A vector representing the plurality of representations 124 is a mathematical structure that can be used to represent multiple data points or variables. In the context of healthcare or any other domain, vectors can be constructed to capture and represent diverse data sets. A vector has a certain number of dimensions that can correspond to the number of data points or variables being represented. Each dimension in a vector corresponds to a particular data point or variable. Each dimension in a vector represents a separate data point or variable. For example, in the domain of healthcare, these data points may include patient demographics (e.g., age, sex), medical history (e.g., condition, surgery), lab results (e.g., cholesterol levels, glucose levels), vital signs (e.g., blood pressure, heart rate), or any other relevant healthcare related information. The values ​​in each dimension of the vector have a corresponding value that represents a particular data point or variable. These values ​​may be numerical, categorical, or symbolic, depending on the nature of the data being represented. In one embodiment, the plurality of representations 124 represented as vectors may be used to communicate a large amount of information about a patient. This may include information regarding the ECG signal 108, multimodal data, health data, structured EHR 120, and the like. In a non-limiting example, the vector-based representation 124 may represent all or a portion of the amplitude, time, waveform, heart rate, and rhythm of the ECG signal 108. Additionally, the vector-based representation 124 may represent all or a portion of the patient's demographics, medical history, past and current diagnoses, past and current medical procedures, medications, lab results, and clinical notes. The vector-based representations 124 may represent diagnostic codes of the structured EHR 120. For example, the dimensions of the vector may correspond to the number of diagnostic codes represented, with each dimension representing a particular diagnostic code.

[0034] With continued reference to FIG. 1 , the plurality of representations 124 may include a first representation, a second representation, and / or a third representation. Each representation of the plurality of representations may be depicted as a vector. As used in this disclosure, a "first representation" refers to a relationship or connection established between the plurality of electrocardiogram signals 108, the textual data, and the structured EHR 112. The representation of the ECG signals with the structured EHR, together with the textual data, may enhance decision-making and care coordination. Healthcare professionals may utilize the combined information to make more informed diagnoses, develop appropriate treatment plans, monitor the progress of the patient, and ensure continuity of care. The availability of both the ECG signals and the textual data in an integrated system facilitates seamless communication and coordination among the health care team involved in the care of the patient, and a "second representation" as used in this disclosure refers to a relationship or connection established between the plurality of electrocardiogram signals 108 and the structured EHR 120. The recorded ECG signals 108 may be integrated into the structured EHR. This integration ensures that the ECG signal 108 becomes part of the patient's longitudinal health record, allowing health care professionals to access and analyze it along with other health information. A "third representation" as used in this disclosure is a relationship or connection established between multiple electrocardiogram signals 108 and textual data. Each representation of the multiple representations 124. The ECG signal 108 is often interpreted by a health care provider, such as a cardiologist or electrocardiography technician, who analyzes the signal to diagnose a cardiac abnormality or condition. Their findings, assessment, and interpretation are documented in textual form within the sEHR 120. The textual data may include a description of the ECG waveform, identification of abnormalities, diagnostic conclusions, and recommended treatment plans.

[0035] Continuing with reference to FIG. 1, the processor 104 can generate the plurality of representations 124 using the representation machine learning model 128. As used in this disclosure, a "representation machine learning model" is a machine learning model configured to generate the plurality of representations 124. The representation machine machine learning model may correspond to a machine learning model or classifier as described below in FIG. 2. Inputs to the representation machine machine learning model can include an ECG signal 108, an EHR 112, a plurality of metadata 116, a structured EHR 120, a plurality of diagnostic codes, a plurality of examples of the representations 124, and the like. Outputs to the representation machine machine learning model can include a plurality of representations 124. Outputs to the representation machine learning model 128 can also include a first representation, a second representation, and a third representation. The representation machine learning model 128 can be configured to generate representations of data from a number of different modalities. The representation training data can include a plurality of data entries including a plurality of inputs that correlate to a plurality of outputs for training the processor with a machine learning process. In one embodiment, the representation training data can include a plurality of structured electronic health record examples and / or ECG signal examples that correlate to the plurality of examples of the representations. In other embodiments, the representation training data can include a plurality of structured electronic health record examples, ECG signal examples, and / or text data examples that correlate to the first representation example. Additionally, the representation training data can include a plurality of structured electronic health record examples and text data examples that correlate to the third representation example. The representation training data can be received from the database 300. The representation training data can include information regarding the ECG signal 108, the EHR 112, a plurality of metadata 116, the structured EHR 120, a plurality of diagnostic codes, a plurality of representation 124 examples, and the like. In one embodiment, the representation training data can be iteratively updated as a function of input and output results of past representation machine learning models or any other machine learning models mentioned throughout this disclosure.The machine learning model may be implemented using, but is not limited to, linear machine learning models such as logistic regression and / or naive Bayes machine learning models, nearest neighbor machine learning models such as k-nearest neighbor machine learning models, support vector machines, least squares support vector machines, Fisher's linear discriminant, quadratic machine learning models, decision trees, boosted trees, random forest machine learning models, and the like.

[0036] Continuing with reference to FIG. 1, the processor 104 can be configured to assign at least one diagnostic label 132 to the user and / or user profile 128 as a function of the plurality of representations 124. As used in this disclosure, a "diagnostic label" is a label used to describe a particular condition, disorder, or illness that affects an individual's health. The diagnostic label 132 can be associated with any particular condition, disorder, or illness related to the heart. In a non-limiting example, the diagnostic label 132 can be associated with conditions related to heart health, such as arteriosclerotic cardiovascular disease (ASCVD), myocarditis, pulmonary hypertension (PH), left ventricular ejection fraction (LVEF), atrial fibrillation in normal sinus rhythm (AFib in NSR), and the like. The processor 104 can assign the diagnostic label 132 to the user as a function of the comparison. The processor 104 can assign the diagnostic label 132 by comparing the current representation 124 with similar representations from other users. The comparison may include comparing the size, location, magnitude, and the like of the representation 124 to similar representations from other similarly located patients. This may also be cross-referenced with a user profile, which may provide additional context for the comparison. The processor 104 may use pattern matching techniques to identify specific patterns or anomalies in the multiple representations 124 to generate the diagnostic label 132. This may include comparing specific segments, intervals, or waveforms of the ECG signal to detect similarities or differences. Cross-correlation, template matching, or dynamic time warping algorithms may be used for this purpose. The processor 104 may perform statistical analysis on various parameters derived from the multiple representations 124 to generate the diagnostic label 132. This may include calculating means, standard deviations, or other statistical measures for specific features or segments of the multiple representations 124. By comparing these statistical parameters, the processor may identify significant differences or similarities between the multiple representations 124.

[0037] With continued reference to FIG. 1 , the processor 104 can generate a diagnostic label using a diagnostic machine learning model. As used in this disclosure, a “diagnostic machine learning model” is a machine learning model configured to assign a diagnostic label 132 to a user. The diagnostic machine learning model can correspond to the machine learning model described below in FIG. 2. Inputs to the diagnostic machine learning model can include an ECG signal 108, an EHR 112, a plurality of metadata 116, a structured EHR 120, a plurality of diagnostic codes, a plurality of representations 124, an example diagnostic label 132, and the like. Outputs to the diagnostic machine learning model can include assigning a diagnostic label 132 aligned with the plurality of representations 124. Diagnostic training data can include a plurality of data entries including a plurality of inputs that correlate to a plurality of outputs for training the processor by a machine learning process. In one embodiment, the diagnostic training data can include a plurality of representations 124 that correlate to the example diagnostic labels 132. The diagnostic training data can be received from a database 300. The diagnostic training data may include information about the ECG signal 108, the EHR 112, metadata 116, the structured EHR 120, diagnostic codes, representations 124, examples of diagnostic labels 132, and the like. In one embodiment, the diagnostic training data may be iteratively updated as a function of input and output results of past diagnostic machine learning models or any other machine learning models mentioned throughout this disclosure. The machine learning models may be implemented using, but are not limited to, linear machine learning models such as logistic regression and / or naive Bayes machine learning models, nearest neighbor machine learning models such as k-nearest neighbor machine learning models, support vector machines, least squares support vector machines, Fisher's linear discriminant, quadratic machine learning models, decision trees, boosted trees, random forest machine learning models.

[0038] With further reference to FIG. 1, the processor may be configured to generate a machine learning model, such as a diagnostic machine learning model, using a naive Bayes classification algorithm. A naive Bayes classification algorithm generates a classifier by assigning class labels to problem instances represented as vectors of element values. The class labels are drawn from a finite set. A naive Bayes classification algorithm may include generating a family of algorithms that assume that, given a class variable, the value of a particular element is independent of the value of any other element. A naive Bayes classification algorithm may be based on Bayes' theorem, expressed as P(A / B)=P(B / A)P(A)÷P(B), where P(A / B) is the probability of hypothesis A given data B, also known as the posterior probability, P(B / A) is the probability of data B given that hypothesis A was true, P(A) is the probability that hypothesis A is true regardless of the data, also known as the prior probability of A, and P(B) is the probability of the data independent of the hypothesis. A naive Bayes algorithm may be generated by first converting the training data into a frequency table. The processor 104 may then calculate a likelihood table by calculating the probabilities of different data entries and classification labels. The processor 104 may utilize a naive Bayes equation to calculate the posterior probability of each class. The class with the highest posterior probability is the predicted outcome. The naive Bayes classification algorithm may include a Gaussian model that follows a normal distribution. The naive Bayes classification algorithm may include a multinomial model that is used for discrete counts. The naive Bayes classification algorithm may include a Bernoulli model that may be utilized when the vectors are binary.

[0039] With further reference to FIG. 1, the processor 104 may be configured to generate a machine learning model, such as a diagnostic machine learning model, using a K-nearest neighbor (KNN) method. As used in this disclosure, "K-nearest neighbor" includes a classification method that utilizes feature similarity to analyze how similar out-of-sample features are to training data and classify input data into one or more clusters and / or categories of features as represented in the training data. This may be performed by representing both the training data and the input data in vector form and using one or more measures of vector similarity to identify classifications in the training data and determine the classification of the input data. The K-nearest neighbor method may include specifying a K value, or a number that instructs the classifier to select the k most similar entries training data for a given sample, determining the most common classifiers among the entries in the database, and classifying the known sample. This may be performed recursively and / or iteratively to generate a classifier that can be used to classify the input data as further samples. For example, an initial set of samples may be run to cover initial heuristics and / or "first guesses" at the outputs and / or relationships, which may be seeded using, without limitation, expert input received according to any process described herein. As a non-limiting example, the initial heuristics may include ranking associations between the inputs and elements of the training data. The heuristics may include selecting a few of the highest ranked associations and / or training data elements.

[0040] Continuing with reference to FIG. 1, generating a k-nearest neighbors method may generate a first vector output including the data entry clusters, generate a second vector output including the input data, and calculate the distance between the first and second vector outputs using any suitable norm, such as cosine similarity, Euclidean distance measure, and the like. Each vector output may be represented, without limitation, as an n-tuple of values, where n is at least two values. Each value in the n-tuple of values ​​may represent a measurement or other quantitative value associated with a given data category or attribute, examples of which are provided in further detail below. The vectors may be represented in an n-dimensional space using, without limitation, an axis for each category of values ​​represented in the n-tuple of values, such that the vector has a geometric direction that characterizes the relative amount of the attributes in the n-tuple compared to each other. Two vectors may be considered equivalent if their directions and / or the relative amount of values ​​in each vector 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 to a vector represented as [1,2,3] for purposes of this disclosure. Vectors may be more similar if their directions are more similar, or more dissimilar if their directions are more divergent. However, vector similarity may alternatively or additionally be determined using the average of similarities between similar attributes, or any other similarity measure appropriate for an n-tuple of any value, or an aggregation of numerical similarities for purposes of loss functions as described in more detail below. Any of the vectors described herein may be scaled so that each vector represents each attribute along an equivalent scale of values. Each vector may be "normalized," or scaled using the Pythagorean norm JPEG2025022872000002.jpg1440 may be divided by a "length" attribute, such as a length attribute l, derived using a iis the attribute number empirical for the vector. Scaling and / or normalization can serve to make vector comparisons independent of the absolute amount of attributes while preserving any dependency on attribute similarity. This can be advantageous, for example, when the cases represented in the training data are represented by different amounts of samples, resulting in proportionally equivalent vectors with divergent values.

[0041] With continued reference to FIG. 1 , the processor 104 may generate a diagnostic report based on the assignment of one or more diagnostic labels 132. As used in this disclosure, a “diagnostic report” is a comprehensive summary of the analysis performed on the multiple representations 124 and the resulting assignment of one or more diagnostic labels 132. The diagnostic report describes essential patient information such as the patient's name, age, sex, and any relevant medical identifiers. This information helps ensure proper identification of the patient and associate diagnostic findings with the correct individual. The diagnostic report may include the date and time of recording of the ECG signal 108, the signal or electrode configuration used, and any specific instructions or conditions during the recording process. These details provide a context for subsequent analysis and interpretation. The report may include technical findings regarding the quality and validity of the multiple representations 124. This may include findings or notes regarding signal quality or technical issues that may have affected the analysis. These findings help evaluate the reliability of the diagnostic conclusion. The report may include diagnostic features from the multiple representations 124 along with other features such as loops, loop direction, loop shape, QRS complex characteristics, ST segment abnormalities, and the like. The report may provide numerical values, graphical representations, or visual descriptions of these features to aid understanding. Based on the analysis of the diagnostic features, the report may include a diagnostic conclusion associated with a diagnostic label 132 that identifies any abnormalities or specific cardiac conditions present in the multiple representations 124. The diagnostic conclusion may be binary (normal vs. abnormal) or multi-class labels representing different cardiac conditions. Each conclusion is supported by an analysis of the extracted features and may include a confidence level or severity rating.

[0042] With continued reference to FIG. 1 , the processor 104 may be configured to generate a plurality of graphical data as a function of the plurality of representations 124. As used in this disclosure, “graphical data” is a visual representation of information that conveys aspects of the plurality of representations 124. Graphical data may refer to any type of data that is visually presented via a graphical representation, such as charts, graphs, diagrams, maps, and other visual aids. These visual representations may represent complex data and are used to communicate information in an easy to understand manner. Graphical data may take many forms depending on the type of data being presented and the intended audience. For example, a line graph may be used to show trends over time for a particular data set, and a pie chart may be used to display the distribution of different categories within a larger data set. Other types of graphical data may include bar graphs, scatter plots, heat maps, network diagrams, and the like. The graphical data may include a graphical representation of one or more elements of the first representation, the second representation, or the third representation. In one embodiment, the graphical data may include a graphical representation of the plurality of representations 124 associated with a current patient. Additionally, the graphical data may include a graphical representation of the plurality of representations 124 associated with a past patient. Representations related to past patients can be generated from the database 300. The graphical data can include a graphical representation of the plurality of representations 124 in vector format. In some embodiments, the graphical data can include plotting the plurality of representations 124 along a continuum. As used in this disclosure, a "continuum" is a spectrum or range of values, qualities, or attributes that exist along a single dimension or scale. A continuum can represent a continuous progression from one pole to another without clear boundaries or distinct categories. In a continuum, there are no clear breakpoints or divisions, instead there is a gradual transition or progression from one end to the other. In some embodiments, a continuum can represent qualitative traits that exist on a spectrum.In a non-limiting example, the continuum may represent one or more features associated with the structured ECG 120, the text data, the multimodal data, the ECG signal 108. The graphical data may include multiple continuums, each continuum representing one or more traits or characteristics of the representation. In some embodiments, multiple continuums may be combined to generate an XY or XYZ plot. The processor 104 may be configured to add labels to the axes, titles, legends, and any other visual elements that provide context to the graphical data. The processor 104 may further be configured to customize the appearance of the data points, lines, or other graphical elements. The graphical data processor 104 may be configured to organize the multiple representations 124 in an appropriate format for rendering. In a non-limiting example, this may include having two sets of values, an independent variable (x-values) that represents a continuum or range, and a dependent variable (y-values) that represents two corresponding aspects of the multiple representations 124. The processor 104 may identify and implement a drawing library to generate the graphical data. Examples of drawing libraries include, but are not limited to, Matplotlib for Python, ggplot for R, or Plotly for JavaScript. The processor 104 can then pass the x and y values ​​to a function in a plotting library dedicated to creating scatter plots or line plots, which generates a plot with data points representing various aspects of the multiple representations 124 along a continuum.

[0043] With continued reference to FIG. 1 , the graphical data includes one or more representation clusters. As used herein, a “representation cluster” is a collection of data points that represent at least one attribute or characteristic of the plurality of representations 124. A representation cluster can include a grouping of data points that represent a collection of similar or related data points in a dataset. In other words, a representation cluster is a subset of data points that exhibit some degree of similarity or proximity to one another while being distinct from other clusters in the dataset. Identifying a representation cluster can be used to reveal patterns, structures, or relationships in the dataset, such as common traits or factors between patients with a common diagnostic label 132. Clusters can be formed based on various criteria, such as proximity in feature space or similarity of attributes. By identifying clusters, the processor 104 can gain insight into the underlying structure of the data and potentially discover meaningful patterns or subgroups in the plurality of representations 124. A representation cluster can include a single attribute of a user, or can include multiple attributes. A representation cluster can include multiple related attributes. In a non-limiting example, a representation cluster may include multiple data points that represent characteristics of the ECG signal 108 along with patient-specific attributes, such as medical history. The processor 104 may identify a representation cluster based on similarity or uniformity for a group of data points. A representation cluster may represent a group of data points that share similar characteristics or properties. For example, a representation cluster may represent a group of patients with similar ECG signals 108 or similar traits shown in the sEHR 120, such as medical history. In some cases, the processor 104 may identify groupings and subgroupings based on the identification of one or more representation clusters 124. A representation cluster 124 may indicate the presence of distinct subpopulations or classes within a dataset. Clusters may reveal patterns or structures in the data that are not immediately apparent. Examining the characteristics of the data points within a cluster may reveal relationships or associations that may be useful for further analysis or decision making.In a non-limiting example, the expression clusters may represent a group of patients who are at high risk for ischemic heart disease and have a family history of the disease. The processor 104 may compare the expression clusters 124 of the current cohort of patents to national averages or national databases to gain additional insight.

[0044] With further reference to FIG. 1, the processor 104 may be configured to display the diagnostic label 132 using the display device 136. As used in this disclosure, a "display device" is a device used to display content. The display device 136 may include a user interface. A "user interface" as used herein is a means by which a user interacts with a computer system, for example, by using input devices and software. A user interface may include a graphical user interface (GUI), a command line interface (CLI), a menu-driven user interface, a touch user interface, a voice user interface (VUI), a form-based user interface, any combination thereof, and the like. A user interface may include a smartphone, a smart tablet, a desktop, or a laptop operated by a user. In one embodiment, a user interface may include a graphical user interface. A "graphical user interface (GUI)" as used herein is a graphical form of a user interface that allows a user to interact with an electronic device. In some embodiments, a GUI may include icons, menus, other visual indicators, or representations (graphics), audio indicators such as primary notations, and display information and associated user controls. A menu may include a list of options and allow a user to select one from them. A menu bar may be displayed horizontally across the screen, such as a pull-down menu. When any option in this menu is clicked, the pull-down menu may be displayed. Menus may include context menus that are only displayed when the user performs a certain action. An example of this is pressing the right mouse button. When this is done, a menu may be displayed under the cursor. Files, programs, web pages, and the like may be represented using small images in the graphical user interface.For example, links to a distributed platform as described in this disclosure may be incorporated using icons. Using icons can be a quick way to open documents, run programs, etc., since clicking on them provides immediate access. Information contained in a user interface may be directly influenced using graphical control elements such as widgets. A "widget" as used herein is a user-controlled element that allows a user to control and change the appearance of an element in a user interface. In this context, a widget can refer to a common GUI element such as a checkbox, button, or scrollbar to an instance of that element, or a customized collection of such elements used for a specific function or application (such as a dialog box for a user to customize the appearance of a computer screen). A user interface control can include software components that a user interacts with through direct manipulation to read or edit information displayed through a user interface. Widgets can be used to display lists of related items, navigate the system using links, tabs, and manipulate data using checkboxes, radio boxes, and the like.

[0045] 2, an exemplary embodiment of a machine learning module 200 capable of performing one or more machine learning processes described in this disclosure is shown. The machine learning module can use the machine learning processes to perform the determination, classification, and / or analysis steps, methods, processes, or the like, as described in this disclosure. As used in this disclosure, a "machine learning process" is a process that uses training data 204 in an automated manner to generate algorithms instantiated in hardware or software logic, data structures, and / or functions executed by a computing device / module to create an output 208 given data provided as input 212. This is in contrast to non-machine learning software programs, where the commands to be executed are predetermined by a user and written in a programming language.

[0046] With further reference to FIG. 2, as used herein, "training data" is data that includes correlations that a machine learning process can use to model relationships between two or more categories of data elements. For example, but not by way of limitation, the training data 204 can include a plurality of data entries, also known as "training examples," each of which represents a set of data elements recorded, received, and / or generated together. The data elements may be correlated by shared presence in a given data entry, proximity in a given data entry, or the like. The plurality of data entries in the training data 204 can represent one or more trends of correlations between categories of data elements. For example, but not by way of limitation, higher values ​​of a first data element belonging to a first category of data elements tend to correlate with higher values ​​of a second data element belonging to a second category of data elements, indicating a possible proportional or other mathematical relationship linking values ​​belonging to the two categories. The plurality of categories of data elements can be associated with the training data 204 according to various correlations. Correlations may indicate causal 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 204 may be formatted and / or organized by categories of data elements, for example, by associating the data elements with one or more descriptors that correspond to the categories of the data elements. As a non-limiting example, training data 204 may include data entered in a standardized format by a person or process, such that an entry of a given data element in a given field in a form may be mapped to one or more descriptors of the category. Elements in training data 204 may be linked to the descriptors of the category by tags, tokens, or other data elements.For example, but not by way of limitation, the training data 204 may be provided in a fixed-length format, a format that links data locations to categories, such as a comma-separated values ​​(CSV) format, and / or a self-describing format, such as extensible markup language (XML), JavaScript Object Notation (JSON), or the like, that allows a process or device to detect the category of the data.

[0047] Alternatively or additionally, and continuing to refer to FIG. 2, the training data 204 may include one or more elements that are unclassified. That is, the training data 204 may not be formatted or may not include descriptors for some elements of the data. Machine learning algorithms and / or other processes may sort the training data 204 according to one or more classifications, for example, using natural language processing algorithms, tokenization, detection of correlation values ​​in the raw data, and the like. Categories may be generated using correlation and / or other processing algorithms. As a non-limiting example, in a corpus of text, phrases that make up a number "n" of compounds, such as nouns modified by other nouns, may be identified according to the statistically significant prevalence of n-grams that contain such words in a particular order. Such n-grams may be classified as elements of language, such as "words," that are tracked similarly to single words, and new categories may be generated as a result of the statistical analysis. Similarly, in a data entry that includes some text data, a person's name may be identified by reference to a list, dictionary, or other summary of terms, allowing for ad-hoc classification by a machine learning algorithm and / or automatic association of the data into a data entry with a descriptor or a given format. The ability to automatically classify data entries may allow the same training data 204 to be applicable to two or more separate machine learning algorithms, as described in more detail below. The training data 204 used by the machine learning module 200 may correlate any input data as described in this disclosure to any output data as described in this disclosure. As a non-limiting illustrative example [describes inputs and outputs that may be used with the present invention].

[0048] With further reference to FIG. 2, the training data may be filtered, sorted, and / or selected using one or more supervised and / or unsupervised machine learning processes and / or models, as described in more detail below. Such models may include, but are not limited to, a training data classifier 216. The training data classifier 216 may include a "classifier," which as used in this disclosure is a machine learning model defined below, such as a data structure representing and / or using a mathematical model, neural net, or program generated by a machine learning algorithm known as a "classification algorithm," which sorts inputs into categories or bins of data and outputs categories or bins of data and / or labels associated therewith, as described in more detail below. The classifier may be configured to output at least one data labeling or otherwise identifying sets of data that are clustered together, found to be close under a distance metric as described below, or similar. The distance metric may include any norm, such as, but not limited to, the Pythagorean norm. The machine learning module 200 can generate the classifier using a classification algorithm, defined as a process in which a computing device and / or any modules and / or components operating therein derive a classifier from training data 204. The classification can be performed using, but is not limited to, linear classifiers such as logistic regression and / or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbor 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. As a non-limiting example, the training data classifier 216 can classify structured electronic health records 120 and ECG signals 108 that correlate to examples of the plurality of representations 124.

[0049] With further reference to FIG. 2, training examples for use as training data may be selected from a population of potential examples according to a cohort associated with the analytical problem, classification task, or the like to be solved. Alternatively or additionally, training data may be selected to span a set of possible situations or inputs for the machine learning model and / or process that will be encountered upon deployment. For example, for each category of input data to a machine learning process or model that may lie within a range of values ​​in a population of phenomena such as, but not limited to, images, user data, process data, physical data, etc., the computing device, processor, and / or machine learning model may select training examples that represent each possible value on such range and / or a representative sample of values ​​on such range. Selection of representative examples may include, for example, selecting training examples in a proportion that is consistent with a statistically determined and / or predicted distribution of such values ​​according to relative frequency, such that values ​​encountered more frequently in the population of data so analyzed are represented by more training examples than values ​​encountered less frequently. Alternatively or additionally, the set of training examples may be compared to a collection of representative values ​​in a database and / or presented to a user such that the process can detect, automatically or via user input, one or more values ​​not included in the set of training examples. The computing device, processor, and / or module may automatically generate the missing training examples by receiving and / or obtaining the missing input and / or output values ​​and correlating the missing input and / or output values ​​with corresponding output and / or input values ​​provided by a user and / or other device, or the like, that are collocated in the data record with the obtained values.

[0050] With further reference to FIG. 2, the computer, processor, and / or module may be configured to sanitize the training data. As used in this disclosure, "sanitizing" the training data is a process in which training examples that prevent the convergence of a machine learning model and / or processing to a useful result are removed. For example, but not limited to, the training examples may include input and / or output values ​​that are outliers from commonly encountered values, such that the machine learning algorithm using the training examples is adapted to less likely amounts as inputs and / or outputs. For example, values ​​that are more than a threshold number of standard deviations from the average value, mean value, or expected value may be eliminated. Alternatively or additionally, one or more training examples may be identified as having low quality data, with "low quality" being defined as having a signal-to-noise ratio below a threshold.

[0051] As a non-limiting example, and with further reference to FIG. 2, images used to train an image classifier or other machine learning model, and / or processes that take images as input or generate images as output, may be rejected if the image quality is below a threshold. For example, and without limitation, a computing device, processor, and / or module may perform blur detection, and rejecting one or more blur detections may be performed by, as a non-limiting example, taking a Fourier transform, or an approximation such as a Fast Fourier Transform (FFT), of the image, and analyzing the distribution of low and high frequencies in the resulting frequency domain representation of the image. The number of high frequency values ​​below a threshold level may indicate blurriness. As a further non-limiting example, blurriness detection may be performed by convolving the image or a channel of the image, or the like, with a Laplacian kernel. This may generate a numerical score that reflects some abrupt changes in intensity shown in the image, such that a high score indicates clarity and a low score indicates blurriness. Blur detection can be performed using gradient-based operators, which measure the gradient or first derivative of the image based on the hypothesis that abrupt changes indicate sharp edges in the image and therefore a lower degree of blur. Blur detection can be performed using wavelet-based operators that exploit the ability of discrete wavelet transform coefficients to describe the frequency and spatial content of an image. Blur detection may also be performed using statistics-based operators that exploit some image statistics as texture descriptors to calculate the focus level. Blur detection can be performed by using discrete cosine transform (DCT) coefficients to calculate the focus level of an image from its frequency content.

[0052] With continued reference to FIG. 2, the computing device, processor, and / or module may be configured to be preconditioned on one or more training examples. For example, but not by way of limitation, if a machine learning model and / or process has one or more inputs and / or outputs that require, transmit, or receive a particular number of bits, samples, or other data units, the elements of the one or more training examples used as or compared to the inputs and / or outputs may be modified to have such number of data units. For example, the computing device, processor, and / or module may convert a smaller number of units, such as in a low-pixel image, to a desired number of units, such as by upsampling and interpolation. As a non-limiting example, a low-pixel image may have 100 pixels, but the desired number of pixels may be 128. The processor may interpolate the low-pixel image to convert the 100 pixels to 128 pixels. It should also be noted that those skilled in the art, upon reading this disclosure, will know various methods for interpolating a smaller number of data units, such as samples, pixels, bits, or the like, to a desired number of such units. In some cases, the set of interpolation rules may be trained with highly detailed inputs and / or outputs and corresponding sets of inputs and / or outputs, downsampled to a smaller number of units, and a neural network or other machine learning model trained to predict interpolated pixel values ​​using the training data. As a non-limiting example, sample inputs and / or outputs, such as sample photos with sample augmented data units (e.g., pixels added between original pixels), may be input to a neural network or machine learning model to output a pseudo-replica sample photo with dummy values ​​assigned to pixels between the original pixels based on the set of interpolation rules. As a non-limiting example, in the context of an image classifier, the machine learning model may have a set of interpolation rules trained with highly detailed images and sets of images downsampled to a smaller number of pixels, and a neural network or other machine learning model trained using those examples to predict interpolated pixel values ​​in a face image context.As a result, the input with sample expanded data units (those added between the original data units, with dummy values) may be run through a trained neural network and / or model, which may pad values ​​to replace the dummy values. Alternatively or additionally, the processor, computing device, and / or module may utilize a sample expander method, a low pass filter, or both. As used in this disclosure, a "low pass filter" is a filter that passes signals with frequencies below a selected cutoff frequency and attenuates signals with frequencies above the cutoff frequency. The exact frequency response of the filter depends on the filter design. The computing device, processor, and / or module may pad data units between the original data units using averaging, such as luma or chroma averaging within the image.

[0053] In some embodiments, with continued reference to FIG. 2, a computing device, processor, and / or module may downsample elements of a training example to a desired smaller number of data elements. As a non-limiting example, a high pixel count image may have 256 pixels, but the desired number of pixels may be 128. The processor may downsample the high pixel count image to convert the 256 pixels to 128 pixels. In some embodiments, the processor may be configured to perform downsampling on the data. Downsampling, also known as decimation, may include removing every Nth entry, all but the Nth entry, or the like, in a series of samples, a process known as "compression" and may be performed, for example, 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 remove compression artifacts.

[0054] With further reference to FIG. 2, the machine learning module 200 may be configured to execute a lazy learning process 220 and / or protocol, which may alternatively be referred to as a “lazy load” or “call on demand” process and / or protocol, whereby machine learning is performed upon receiving inputs that are converted into outputs by combining the inputs and training set to derive an algorithm that is used to generate outputs on demand. For example, an initial set of simulations may be run to cover initial heuristics and / or “first guesses” at the outputs and / or relationships. As a non-limiting example, the initial heuristics may include ranking of associations between inputs and elements of the training data 204. The heuristics may include selecting a number of the highest ranked associations and / or elements of the training data 204. The lazy learning may implement any suitable lazy learning algorithm, including but not limited to K-nearest neighbors, lazy naive Bayes algorithms, or the like. Those skilled in the art will recognize, upon review of this disclosure in its entirety, a variety of lazy learning algorithms that may be applied to generate outputs as described in this disclosure, including but not limited to lazy learning applications of machine learning algorithms as described in more detail below.

[0055] Alternatively or additionally, with continued reference to FIG. 2, the machine learning model 224 may be generated using a machine learning process as described herein. A "machine learning model" as used herein is a data structure that represents and / or instantiates a mathematical and / or algorithmic representation of a relationship between inputs and outputs, generated and stored in memory using any machine learning process, including but not limited to any process as described above. The inputs are submitted to the machine learning model 224 once created, and the machine learning model generates an output based on the derived relationship. For example, but not by way of limitation, a linear regression model generated using a linear regression algorithm may calculate a linear combination of the input data using coefficients derived during the machine learning process to calculate the output data. As a further non-limiting example, the machine learning model 224 may be generated by creating an artificial neural network, such as a convolutional neural network, that includes an input layer of nodes, one or more hidden layers, and an output layer of nodes. The connections between nodes can be created through a process of "training" the network, where elements from a training data 204 set are applied to the input nodes, and then an appropriate training algorithm (e.g., Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithm) is used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce desired values ​​at the output nodes. This process is sometimes referred to as deep learning.

[0056] With further reference to FIG. 2, the machine learning algorithm may include at least a supervised machine learning process 228. At least a supervised machine learning process 228 as defined herein includes an algorithm that receives a training set that relates some inputs to some outputs and seeks to generate one or more data structures that represent and / or instantiate one or more mathematical relationships that relate the inputs to the outputs, each of the one or more mathematical relationships being optimal according to some criteria specified to the algorithm using some scoring function. For example, the supervised learning algorithm may include the structured electronic health record 120 and the ECG signal 108 as described above as inputs, the representations 124 as outputs, and a scoring function that represents the desired form of the relationship to be found between the inputs and the outputs. The scoring function may, for example, seek to maximize the probability that a given input and / or combination of component inputs is associated with a given output and minimize the probability that a given input is not associated with a given output. The scoring function can be expressed as a risk function that represents the "expected loss" of the algorithm with respect to the input to the output, where the loss is calculated as an error function that represents the degree to which the prediction produced by the relationship is inaccurate when compared to a given input / output pair provided in the training data 204. Those skilled in the art will recognize upon review of this disclosure in its entirety the various possible variations of at least a supervised machine learning process 228 that may be used to determine the relationship between the input and the output. A supervised machine learning process can include a classification algorithm as defined above.

[0057] With further reference to FIG. 2, training the supervised machine learning process may include iteratively updating coefficients, biases, weights based on, but not limited to, an error function, an expected loss, and / or a risk function. For example, the output generated by the supervised machine learning model using the input examples in the training examples may be compared to the output examples from the training examples. An error function may be generated based on the comparison, which may include any error function suitable for use in any machine learning algorithm described in this disclosure, including the squared difference between one or more sets of comparison values ​​or the like. Such an error function may be used to update one or more weights, biases, coefficients, or other parameters of the machine learning model via any suitable process, including but not limited to a gradient descent process, a least squares process, and / or other processes described in this disclosure. This may be done iteratively and / or recursively to gradually adjust such weights, biases, coefficients, or other parameters. The update may be performed using one or more backpropagation algorithms in the neural network. The iterative and / or recursive updating of weights, biases, coefficients, or other parameters as described above may be performed until the currently available training data is exhausted and / or a convergence test is passed, where a "convergence test" is a test of a condition selected as indicating that the model and / or its weights, biases, coefficients, or other parameters have reached a certain degree of accuracy. The convergence test may, for example, compare the difference between two or more successive error or error function values, where a difference below a threshold amount may be taken to indicate convergence. Alternatively or additionally, one or more error and / or error function values ​​evaluated in a training iteration may be compared to a threshold value.

[0058] With further reference to FIG. 2, a computing device, processor, and / or module may be configured to perform the methods, method steps, sequence of method steps, and / or algorithms described with reference to this figure in any order and to any degree of repetition. For example, a computing device, processor, and / or module may be configured to repeatedly perform a single step, sequence, and / or algorithm until a desired or commanded result is achieved. The repetition of a step or sequence of steps may be performed iteratively and / or recursively using the output of a previous iteration as input to a subsequent iteration, and the inputs and / or outputs of the iterations may be aggregated to produce an aggregated result, a decrease or decrement of one or more variables, such as a global variable, and / or a division of a larger processing task into a set of smaller processing tasks that are iteratively addressed. A computing device, processor, and / or module may perform any step, sequence of steps, or algorithm in parallel, such as performing two or more steps simultaneously and / or substantially simultaneously using two or more parallel threads, processor cores, or the like. The division of tasks among parallel threads and / or processes may be performed according to any protocol suitable for dividing tasks among iterations. Those skilled in the art will recognize upon reviewing this disclosure in its entirety the various ways in which processes, sequences of processes, processing tasks, and / or data can be subdivided, shared, or otherwise processed using iterative, recursive, and / or parallel processing.

[0059] With further reference to FIG. 2, the machine learning algorithm may include at least an unsupervised machine learning process 232. As used herein, an unsupervised machine learning process is a process that derives inferences in a dataset without regard to labels. As a result, an unsupervised machine learning process is free to discover any structure, relationships, and / or correlations provided in the data. An unsupervised process may not require a response variable. An unsupervised process may be used to find patterns of interest and / or inferences between variables, determine the degree of correlation between two or more variables, or the like.

[0060] With further reference to FIG. 2, the machine learning module 200 can be designed and configured to create the machine learning model 224 using techniques for developing linear regression models. The linear regression model can include ordinary least squares regression, which aims to minimize the square of the difference between the predicted outcome and the actual outcome according to an appropriate norm for measuring such difference (e.g., vector space distance norm). To improve the minimization, the coefficients of the resulting linear equation can be modified. The linear regression model can include a ridge regression method, where the function to be minimized includes a least squares function and a term that multiplies the square of each coefficient by a scalar amount to penalize large coefficients. The linear regression model can include a least absolute shrinkage and selection operator (lasso) model, where ridge regression is combined with multiplying the least squares term by a coefficient of 1 divided by twice the number of samples. The linear regression model can include a multitask lasso model, where the norm applied to the least squares term of the lasso model is the Frobenius norm, which is equivalent to the square root of the sum of the squares of all the terms. The linear regression model may include an elastic net model, a multitask elastic net model, a minimum 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 aggression algorithm, a robust regression model, a Huber regression model, or any other suitable model that may occur to one of skill in the art upon reviewing the entirety of this disclosure. In one embodiment, the linear regression model may be generalized to a polynomial regression model, whereby a polynomial (e.g., quadratic, cubic, or higher order equation) that provides the best predicted output / actual output fit is found. As would be apparent to one of skill in the art upon reviewing the entirety of this disclosure, methods similar to those described above may be applied to minimize the error function.

[0061] With continued reference to FIG. 2 , the machine learning algorithm may include, but is not limited to, linear discriminant analysis. The machine learning algorithm may include quadratic discriminant analysis. The machine learning algorithm may include kernel ridge regression. The machine learning algorithm may include support vector machines (including but not limited to support vector classification based regression processes). The machine learning algorithm may include stochastic gradient descent algorithms, including classification and regression algorithms based on stochastic gradient descent. The machine learning algorithm may include nearest neighbor algorithms. The machine learning algorithm may include various forms of latent space regularization, such as variational regularization. The machine learning algorithm may include Gaussian processes, such as Gaussian process regression. The machine learning algorithm may include cross decomposition algorithms, including partial least squares and / or canonical correlation analysis. The machine learning algorithm may include naive Bayes methods. The machine learning algorithm may include decision tree based algorithms, such as decision tree classification or regression algorithms. The machine learning algorithm may include ensemble methods, such as bagging meta-estimators, forests of randomized trees, adaboost, gradient tree boosting, and / or voting classifier methods. The machine learning algorithms may include neural net algorithms, including convolutional neural net processes.

[0062] With further reference to FIG. 2, the machine learning model and / or process may be deployed or instantiated by being incorporated into a program, device, system, and / or module. For example, but not limited to, the machine learning model, neural network, and / or some or all of the parameters thereof may be stored and / or deployed in any memory or circuit. Parameters such as coefficients, weights, and / or biases may be stored as circuit-based constants such as an array of wires and / or binary inputs and / or outputs set to logic "1" and "0" voltage levels in a logic circuit to represent numbers according to any suitable encoding system, including 2's complement or the like, or may be stored in any volatile and / or non-volatile memory. Similarly, mathematical operations and data inputs and / or outputs to or from the model, neural network layer, or the like may be instantiated in hardware circuits and / or in the form of instructions in firmware, machine code such as binary opcode instructions, assembly language, or any higher-level programming language. Any technique for hardware and / or software instantiation of memories, instructions, data structures, and / or algorithms may be used to instantiate the machine learning processes and / or models, including, but not limited to, the manufacture and / or configuration of non-reconfigurable hardware elements, circuits, and / or modules, such as ASICs; the manufacture and / or configuration of reconfigurable hardware elements, circuits, and / or modules, such as, but not limited to, FPGAs; the manufacture and / or configuration of non-reconfigurable and / or configurable non-rewriteable memory elements, circuits, and / or modules, such as, but not limited to, non-rewriteable ROMs; the manufacture and / or configuration of reconfigurable and / or rewriteable memory elements, circuits, and / or modules, such as, but not limited to, rewriteable ROMs or other memory technologies described in this disclosure; and / or the manufacture and / or configuration of any computing device and / or components thereof described in this disclosure, in any combination thereof.Such deployed and / or instantiated machine learning models and / or algorithms may receive inputs from, and generate outputs to, any other processes, modules, and / or components described in this disclosure.

[0063] 2, any process of training, retraining, deployment, and / or instantiation of any machine learning model and / or algorithm may be performed and / or repeated after initial deployment and / or instantiation to modify, refine, and / or improve the machine learning model and / or algorithm. Such retraining, deployment, and / or instantiation may be performed as a periodic or periodic process, such as retraining, deployment, and / or instantiation at regular elapsed periods, after some measure of the amount of bytes or other measures of data processed, number of uses or executions of the processes described in this disclosure, or the like, and / or according to a software, firmware, or other update schedule. Alternatively or additionally, retraining, deployment, and / or instantiation may be event-based, triggered by, but not limited to, user input indicating suboptimal or otherwise problematic performance, and / or by an automated field testing and / or auditing process, which may compare the output of the machine learning model and / or algorithm and / or its error and / or error function to any threshold, convergence test, or the like, and / or compare the output of the 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 the receipt and / or generation of one or more new training examples. Some new training examples may be compared to a preconfigured threshold, and exceeding the preconfigured threshold may trigger retraining, deployment, and / or instantiation.

[0064] With further reference to FIG. 2, retraining and / or additional training can be performed using any process for training described above, using any current or previously deployed version of the machine learning model and / or algorithm as a starting point. Training data for retraining may be collected, pre-processed, screened, classified, sanitized, or otherwise processed according to any process described in this disclosure. Training data can include, but is not limited to, training examples including inputs and correlation outputs 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 can be modified and / or labeled according to user feedback or other processes to indicate desired results, and / or have actual or measured results from a process being modeled and / or predicted by the system, module, machine learning model or algorithm, apparatus, and / or method as “desired” results to be compared to the output of the training process as described above.

[0065] Redeployment may be performed using any reconfiguration and / or rewriting of reconfigurable and / or rewritable circuitry and / or memory elements, or redeployment may be performed by the creation of new hardware and / or software components, circuits, instructions, or the like, which may be added to and / or replace existing hardware and / or software components, circuits, instructions, or the like.

[0066] With further reference to FIG. 2, one or more of the processes or algorithms described above may be performed by at least a dedicated hardware unit 232. For purposes of this figure, a "dedicated hardware unit" is a hardware component, circuitry, or the like other than a main control circuit and / or processor that performs the method steps described in this disclosure that are specifically designated or selected to perform one or more specific tasks and / or processes described with reference to this figure, such as, but not limited to, preconditioning and / or sanitizing training data and / or training machine learning algorithms and / or models. The dedicated hardware unit 232 may include hardware units that can efficiently use pipelining, parallel processing, or the like to perform iterative or batch calculations, such as matrix-based calculations to update or adjust parameters, weights, coefficients, and / or biases of machine learning models and / or neural networks. Such hardware units may be optimized for such processes, for example, by including dedicated circuitry for matrix and / or signal processing operations, including multiple arithmetic and / or logic circuit units, such as multipliers and / or adders or the like, that can operate simultaneously and / or in parallel. Such dedicated hardware units 232 may include, but are not limited to, graphical processing units (GPUs), dedicated signal processing modules, FPGAs, or other reconfigurable hardware configured to instantiate parallel processing units for one or more specific tasks, and a computing device, processor, apparatus, or module may be configured to instruct the one or more dedicated hardware units 232 to perform one or more operations described herein, such as evaluation of model and / or algorithmic outputs, one-time or iterative updates to parameters, coefficients, weights, and / or biases, and / or any other operations, such as vector and / or matrix operations, described in this disclosure.

[0067] Referring now to FIG. 3, an exemplary electrocardiogram database 300 is illustrated by a block diagram. In one embodiment, any past or current version of any data disclosed herein may be stored in the electrocardiogram database 300, including, but not limited to, the ECG signal 108, the EHR 112, metadata 116, the structured EHR 120, diagnostic codes, representations 124, diagnostic labels 132, and the like. The processor 104 may be communicatively coupled to the electrocardiogram database 300. For example, in some cases, the database 300 may be local to the processor 104. Alternatively, or additionally, in some cases, the database 300 may be remote from the processor 104 and in communication with the processor 104 via one or more networks. The networks may include, but are not limited to, a cloud network, a mesh network, or the like. By way of example, a "cloud-based" system, as that term is used herein, may refer to a system that includes software and / or data that is stored, managed, and / or processed not on a local server or personal computer, but on a network of remote servers hosted in the "cloud," e.g., via the Internet. A "mesh network," as used in this disclosure, is a local network topology in which the infrastructure processor 104 directly, dynamically, and non-hierarchically connects to as many other computing devices as possible. A "network topology," as used in this disclosure, is an arrangement of elements of a communications network. The electrocardiogram database 300 may be implemented as a key-value lookup database, such as, but not limited to, a relational database, a NOSQL database, or any other format or structure for use as a database that one of ordinary skill in the art would recognize as appropriate upon review of this disclosure in its entirety. The electrocardiogram database 300 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. The electrocardiogram database 300 may include a plurality of data entries and / or records, as described above.Data entries in a database may be flagged or linked with one or more additional elements of information, which may be reflected in data entry cells and / or linked tables, such as tables related by one or more indexes in a relational database. Those of skill in the art, upon review of this disclosure in its entirety, will recognize the various ways in which data entries in a database may store, retrieve, organize, and / or reflect the data and / or records used herein, as well as categories and / or collections of data consistent with this disclosure.

[0068] Referring now to FIG. 4, an exemplary embodiment of a neural network 400 is shown. A neural network 400, also known as an artificial neural network, is a network of "nodes" or data structures having one or more inputs, one or more outputs, and a function that determines the output based on the inputs. Such nodes can be organized into a network, such as, but not limited to, a convolutional neural network, including an input layer of nodes 404, one or more hidden layers 408, and an output layer of nodes 412. The connections between the nodes can be created through a process of "training" the network, where elements from a training data set are applied to the input nodes, and then a suitable training algorithm (e.g., Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is used to adjust the connections and weights between nodes in adjacent layers of the neural network to generate desired values ​​for the output nodes. This process is sometimes referred to as deep learning. The connections may be made only from the input nodes to the output nodes in a "feed-forward" network, or the output of one layer may be fed back to the input of the same or a different layer in a "recurrent network." As a further non-limiting example, a neural network can include a convolutional neural network that includes an input layer of nodes, one or more hidden layers, and an output layer of nodes. A "convolutional neural network," as used in this disclosure, is a neural network in which at least one hidden layer is a convolutional layer that convolves the input to that layer with a subset of the inputs known as "kernels," along with one or more additional layers, such as pooling layers, fully connected layers, and the like.

[0069] 5, an exemplary embodiment of a node of a neural network is shown. The node has a number of inputs x that can receive values ​​from other nodes and / or inputs to the neural network that contains the node. i A node can include, but is not limited to, each input x i Weight w multiplied byi A weighted sum of the inputs can be performed using . Additionally or alternatively, a bias b can be added to the weighted sum of the inputs such that an offset is added to each unit in the neural network layer that is independent of the input to the layer. The weighted sum can then be input to a function φ, which can generate one or more outputs y. i The weight w applied to i can indicate whether an input is “excitatory”, e.g., by having a large corresponding weight, indicating that the input has a strong influence on one or more outputs y, and / or “inhibitory”, e.g., by having a small corresponding weight, indicating that the input has a weak influence on another input y. i The value of may be determined by training a neural network using training data, which may be performed using any suitable process, such as those described above.

[0070] 6A-6F, an exemplary embodiment of a plurality of models, such as one or more models, that can be used as a representational machine learning model as described above and that can be used to generate each of the representations of a plurality of representations, is shown. The processor 104 can obtain a plurality of ICD diagnosis codes, ICD procedure codes, and medication prescriptions that have associations with at least 50 patients and are considered in the vocabulary. Since the ICD-9 codes are different from the ICD-10 codes, but the underlying textual descriptions are similar, the processor 104 can perform a mapping from ICD-9 to ICD-10 to maintain the same phenotypic information. Finally, the ICD-10 diagnosis codes can be shortened to three characters, since retaining more than four characters provides little or no extra information for extensive pre-training. A vocabulary of size 28593 is built based on the medication prescriptions, ICD-10 procedure codes, and abbreviated ICD-10 diagnosis codes. To create a structured EHR sequence for the pre-training processor 104 of the Structured EHR-BERT model, one sequence of 512 consecutive medical codes can be randomly selected from a given patient's timeline.

[0071] 6B, the first representation may be generated using a multimodal versatile network (MMV) that applies contrastive learning to video, audio, and text multimodal data under the assumption that video and audio modalities are more granular than text modality. MMV assumes that applying a contrastive loss to a shared embedding space does not preserve the specificity of each domain, so two embedding spaces are learned: a fine-grained space where video and audio match, and a coarse-grained space where text matches the video and audio domains.

[0072] 6B , in some cases, the first representation may not exhibit the same level of granularity as the third and second representations. The first representations within a given time window surrounding the acquisition of the ECG signal 108 may have their timestamps rounded or trimmed based on the input length accepted by the corresponding encoder. This may cause the information granularity to differ between the third and second representations. MMV embeds the ECG signal 108 into the fine-grained unified third representation embedding space Ω es The structured EHR120 in and the first representation of the coarse integration Ω set may be used to compare the first representation in

[0073] Still referring to FIG. 6B, the data set is a triplet {(X s i ,X e i ,X t i )}Mi=1, where S=X s X e X t X s i is the structured EHR sequence of the i-th sample, and X e i is the ECG waveform of the i-th sample, and X t i is the text sequence of the i-th sample, and M is the total number of samples in the training set. s , X e and X t indicate the structured EHR, ECG and text domains, respectively.

[0074] With continued reference to FIG. 6B, in a non-limiting example, E m :X m →R dm Let Ω be a parameterized model mapping from modality m to a modality-specific embedding of dimension dm, where m can be s, e, t for structured EHR, ECG, and text, respectively. sLet P be the shared space between different modalities onto which the modality-specific representations are projected to maximize or minimize the alignment between different modalities using a contrastive loss objective. To obtain the modality-specific representations, the processor 104 can use any suitable neural network architecture, such as, but not limited to, a residual neural network (ResNet) architecture, other deep learning network architectures, and / or recurrent neural network architectures customized for one dimension of an ECG encoder, such as, but not limited to, the structured EHR-BERT encoder as described above for the structured EHR modality, and the GatorTron encoder for the text modality. The modality-specific representations may be projected into the shared space using a two-layer fully connected network. In another example, P m→s :R dm →R ds From the representation of modality m to the shared space Ω s The processor 104 may apply a contrast loss between the ECG and the structured EHR in the ECG-structured EHR integrated embedding space, and a contrast loss between the ECG and the text in the structured EHR-ECG-text integrated embedding space, such that granularity is maintained.

[0075] With continued reference to FIG. 6B, in another embodiment, V m i X m i Let E be the modality-specific encoder m The representation obtained by passing m-s i V m i Projection network P m→s The shared space Ω obtained by passing s X in m and then, in a non-limiting example, JPEG2025022872000003.jpg8562 is satisfied. The processor 104 may assume that all the above expressions are l2-normalized. The processor 104 may assume, but is not limited to, two l2-normalized vectors x, y ∈ R d One can define a similarity metric, such as the cosine similarity s(x,y) between as. s(x,y)=x T y In a non-limiting example, for a given mini-batch of size N, τ ∈ R + is the temperature parameter, and L es Let be the contrast loss between ECG and structured EHR, and L i e→s Let L be the ECG-to-structured EHR-directed contrast loss, and L i s→e Let be the contrast loss from the structured EHR to the ECG. And JPEG2025022872000004.jpg55122 Meet the following.

[0076] L Contrast loss between ECG and text et Let L be the contrast loss from ECG to text. i e→t Let L be the contrast loss from the text to the ECG. i t→e Then, JPEG2025022872000005.jpg51106 and the combination gives the overall loss as follows: L=L es +L et where λes and λet are tunable hyperparameters ∈[0,1].

[0077] Still referring to FIG. 6C, (X e , X s To create an ECG-structured EHR pair, the processor 104 calculates the ECG of a given patient, eThe processor may select a sequence of medical codes that are constrained to a time range of 1 year prior to and 1 year after the ECG acquisition timestamp, and consider all ICD diagnosis codes, ICD procedure codes, and medication prescriptions associated with that patient within the time period of 1 year prior to and 1 year after the ECG acquisition timestamp. The medical codes restricted to this time range are arranged sequentially to form an initial structured EHR input sequence to the Structured EHR-BERT model. The processor may use a maximum sequence length of 200 medical codes as input to the Structured EHR-BERT encoder. If the structured EHR sequence length can be less than 200, the initial structured EHR input sequence with zeros, and if the structured EHR sequence length is greater than 200, it may trim it by considering the 200 medical codes closest to the ECG acquisition timestamp to form the final X s Get the.

[0078] In the ECG-structured EHR model, the processor 104 pairs the ECG with the structured EHR data and computes the joint ECG-structured EHR embedding space Ω, as described in more detail above. es We apply multimodal contrastive learning to JPEG2025022872000006.jpg4854 The contrast loss between ECG and structured EHR is L es , the contrast loss from ECG to the structured EHR is L i e→s , the contrast loss from the structured EHR to the ECG is L i s→e Then, the loss of the ECG-structured EHR model is given by: JPEG2025022872000007.jpg16116 The contrast loss between ECG and structured EHR can be applied to the ECG-sEHT joint embedding space, where JPEG2025022872000008.jpg38102 is satisfied.

[0079] Selected ECG, X eFor k, the processor 104 can form multiple ECG-text pairs as an intermediate step. The text data can include patient notes. The processor 104 can select the one report that is closest in time to the ECG acquisition date and pair it with the ECG, i.e., the processor 104 can select x reports that come from different sources (k=ECG report, ECHO report, clinical document, etc.) t k Pair with (X e ,X t k ) is formed. X t k When forming the ECG acquisition timestamp, the processor 104 may use only reports created within 30 days after the ECG acquisition timestamp, as evaluated by the processor 104 with reports generated in a time interval of one year before and after the ECG acquisition timestamp, except in the case of entity linkage (described below).

[0080] These interim reports, X t k , the processor 104 can be involved in an entity selection process, where the entities are keywords medically related to the ECG being studied. By detecting those sentences in the notes that contain entities that can be selected from a predefined list, the processor 104 can eliminate training on irrelevant data and improve the speed and potentially the performance of the representation created.

[0081] The processor 104 selects the closest intermediate report, X, that does not contain the entity. t k , followed by randomly selecting one of these truncated intermediate reports to pair with the ECG. In the concatenation experiment, the processor 104 can remove those sentences from the nearest intermediate report that does not contain the entity, but then select the last note, X, that is paired with the ECG. t In the entity concatenation experiment, the processor 104 concatenates all the truncated intermediate reports to form the last note, X tTo form a,c,i,n,, we may focus only on the entities themselves, rather than on the sentences that contain them.,In the latter two experiments, the concatenation follows a priority order in which,ECG reports precede ECHO reports, which in turn precede clinical,,microbiological, pathological, radiological and surgical notes.

[0082] Continuing to refer to FIG. 6D, in the ECG-text model, the processor 104 can pair the electrocardiogram signal with unstructured text data obtained from various medical sources, including ECG reports, ECHO reports, pathology reports, radiology reports, microbiology reports, and clinical documents, which are collectively referred to as patient notes in this work. The processor 104 can then generate a joint ECG-text embedding space Ω et Contrastive learning can be applied between ECG and text in the JPEG2025022872000009.jpg4953 In one embodiment, L et is the contrast loss between ECG and text, and L i e→t is the contrast loss from ECG to text, and L i t→e is the contrast loss going from text to ECG. Then the loss of the ECG-text model is given by: JPEG2025022872000010.jpg34115 Downstream Tasks Numbers for ECG and Unique Patients per Cohort. JPEG2025022872000011.jpg1798

[0083] With continued reference to FIGS. 6A-6D, the ECG representations learned using multiple models can be evaluated on various downstream tasks. The processor 104 can use the representations obtained using the three models presented in this disclosure as input to a logistic regression architecture to train various linear models for disease classification tasks. These diseases can include Arteriosclerotic Cardiovascular Disease (ASCVD), Myocarditis, Pulmonary Hypertension (PH), Left Ventricular Ejection Fraction (LVEF), Atrial Fibrillation in Normal Sinus Rhythm (AFib in NSR). The processor 104 can compare the performance of the linear classification model to two baseline supervised learning models. The first one can be a neural net trained from scratch using random initialization of its weights, and the second one can be a massive multitask learning ("MTL") model, described below. The processor 104 can evaluate the performance (AUC) of the various models on the linear classification task trained over different disease cohorts and different fractions of the training set, i.e., 1%, 10%, 100%.

[0084] 6A-6D, all models across diseases show improvement over models trained from scratch with random initialization. The ECG-StructuredEHR model may be the best performing model overall with slight degradation from the large ECG-MTL model for LVEF and PH diseases. The difference between the ECG-StructuredEHR model and the ECG-Text model varies between 1-2%.

[0085] 6A-6D, in high data environments such as PH, LVEF, and AFib in NSR, the performance drop between models trained on the full training data (100%) and ob 10% of the training data may not be significant. The large drop in performance of the 1% subset in diseases such as myocarditis can be explained by the small cohort size even with the full training set.

[0086] With continued reference to Figures 6A-6D, using the ECG representations obtained from the ECG-structured EHR, processor 104 observes that a linear classification model trained on 10% of the training data across all diseases achieves performance comparable to or better than that of a random weight initialization model trained on the full training data set, demonstrating the effectiveness of the learned representations for label efficiency.

[0087] Continuing with reference to FIGS. 6A-6D, the processor 104 can observe that the representations learned via self-supervised learning techniques help to better distinguish data originating from the distribution under consideration. The processor 104 can demonstrate this using representations obtained from our EHR model to distinguish between two different ECG data sets. The processor 104 can employ a proprietary ECG pulmonary hypertension (PH) cohort as the "In Distribution" and Holter ECGS from the open source St. Petersburg INCART 12-lead arrhythmia database as the "Out Distribution". The processor 104 can train a CNN network (PH model) in a supervised setting on our PH training data to compare the performance of both representations. The processor 104 can use three metrics to determine whether the data is in-distribution or out-distribution: relative Mahalanobis distance (RMD), class-conditional Mahalanobis distance (CCMD), and clean-lab out-distribution. Relative Mahalanobis distance is based on the Mahalanobis distance of embeddings from the closest predicted class, while CleanLab uses a K-nearest neighbors-based approach to distinguish between in-distribution and out-of-distribution samples. We use representations from the EHR model and the PH model and show that the rejection rate at different significance levels is much higher when the EHR model representation is used. The results may show that the general ECG representation is better at detecting out-of-distribution data than the model-specific representation.

[0088] Continuing with reference to FIGS. 6A-6D, the processor 104 may be configured to identify the quality of the multiple representations 124. The multiple representations 124 are said to be good if they satisfy several criteria. The criteria may include expressiveness, abstraction and invariance, and disentanglement. Expressiveness may include the ability of the multiple representations 124 to represent a large number of input configurations. Abstraction and invariance may include the ability of the multiple representations 124 to encode high-level information and thus may be invariant to small local variations in the data. Disentanglement may include the ability to learn all explanatory features while maintaining orthogonality of distinct factors. The quality of the multiple representations 124 may be quantified by the performance improvement it brings in downstream tasks, although there is often a trade-off between good performance across a wide range of tasks and good performance in specialized tasks. When a representation is learned via a proxy task, such as similarity in contrastive learning, the performance of the proxy task may serve as a metric to measure the quality of the representation.

[0089] 6E-6F, the representation can be used to better and more quickly cluster similar data samples. We demonstrate this by comparing clustering on a PH cohort obtained using the EHR model and the PCLR model representation. The processor 104 can execute a density-based clustering algorithm HDBSCAN that runs on the UMAP space of positive ECGs. Clusters i, C i The cluster strength of is given by: JPEG2025022872000012.jpg1861 where Pi is the number of positive ECGs in the i-th cluster and Ni is the number of negative ECGs in the i-th cluster. The cluster strength values ​​above quantify the proportion of positives along with the total number of ECGs in a particular cluster. We use the overall cluster strength to compare the clustering ability of the two sets of embeddings. JPEG2025022872000013.jpg2173 Figures 6E-F show the clustering results for the EHR model and PCLR model embeddings, respectively. The former has an overall cluster strength of 0.5149, while the PCLR embedding cluster has an overall strength of 0.3125.

[0090]

[0031] Referring now to Figure 7, there is shown a flow diagram of an exemplary method 700 for generating a diagnostic label. At step 705, the method 700 includes receiving, using at least one processor, a plurality of electrocardiogram signals from a user. This may be implemented as described with reference to Figures 1-7.

[0091] 7, at step 710, the method 700 includes receiving, using at least one processor, a plurality of electronic health records from a user, the plurality of electronic health records including a plurality of metadata. This may be implemented as described with reference to FIGS. 1-7. In one embodiment, the plurality of metadata includes a plurality of text data. In another embodiment, the plurality of electronic medical records includes a plurality of health data.

[0092] 7, at step 715, the method 700 includes using at least one processor to generate a plurality of structured electronic health records using the plurality of electronic health records. This may be implemented as described with reference to FIGS. 1-7. In one embodiment, generating the plurality of structured electronic health records may include classifying the plurality of health data into a plurality of diagnostic codes. In another embodiment, generating the plurality of structured electronic health records includes using a structure classifier to generate the plurality of structured electronic health records.

[0093] 7, at step 720, the method 700 includes using at least one processor to generate a plurality of representations as a function of the plurality of electrocardiogram signals and the plurality of structured electronic health records using a representational machine learning model, which may be implemented as described with reference to Figures 1-7. In one embodiment, the plurality of representations may include a first representation, a second representation, or a third representation.

[0094] 7, at step 725, the method 700 includes generating, using at least one processor, a diagnostic label as a function of the plurality of representations. This may be implemented as described with reference to FIGS. 1-7. The method may further include generating, using at least one processor, a diagnostic report as a function of the diagnostic label. In some embodiments, the method may be configured to generate, using at least one processor, a plurality of graphical data as a function of the plurality of representations, and to identify, using at least one processor, one or more representation clusters as a function of the plurality of graphical data.

[0095] 7, at step 720, the method 700 includes using the at least one processor to display the diagnostic label using a display device, which may be implemented as described with reference to FIGS.

[0096] It should be noted that any one or more of the aspects and embodiments described herein may be conveniently implemented using one or more machines (e.g., one or more computing devices utilized as user computing devices for electronic documents, one or more server devices, such as document servers, etc.) programmed in accordance with the teachings herein, as would be apparent to one skilled in the computer arts. As would be apparent to one skilled in the software arts, appropriate software coding may be readily produced by skilled programmers based on the teachings of the present disclosure. The above aspects and implementations using software and / or software modules may also include appropriate hardware to assist in the implementation of the machine-executable instructions of the software and / or software modules.

[0097] Such software may be a computer program product using a machine-readable storage medium. A machine-readable storage medium may be any medium capable of storing and / or encoding a set of instructions for execution by a machine (e.g., a computing device) and causing the machine to perform any one of the methodologies and / or embodiments described herein. Examples of machine-readable storage media include, but are not limited to, magnetic disks, optical disks (e.g., CDs, CD-Rs, DVDs, DVD-Rs, etc.), magneto-optical disks, read-only memory "ROM" devices, random access memory "RAM" devices, magnetic cards, optical cards, solid-state memory devices, EPROMs, EEPROMs, and any combination thereof. A machine-readable medium as used herein is intended to include a single medium as well as a collection of physically separate media, such as a compact disk in combination with a computer memory or a collection of one or more hard disk drives. As used herein, a machine-readable storage medium does not include a transitory form of signal transmission.

[0098] Such software may also include information (e.g., data) carried as a data signal on a data carrier, such as a carrier wave. For example, the machine-executable information may be included as a data carrier signal embodied in a data carrier, which signal encodes a set of instructions, or portions thereof, for execution by a machine (e.g., a computing device), and any associated information (e.g., data structures and data) that causes the machine to perform any one of the methodologies and / or embodiments described herein.

[0099] Examples of computing devices include, but are not limited to, e-book reading devices, computer workstations, terminal computers, server computers, handheld devices (e.g., tablet computers, smartphones, etc.), web appliances, network routers, network switches, network bridges, any machine capable of executing a set of instructions that specify an action that the machine is to take, and any combination thereof. In one example, a computing device may include and / or be included in a kiosk.

[0100] 8 illustrates a schematic diagram of one embodiment of a computing device in the exemplary form of a computer system 800 upon which a set of instructions for causing a control system to perform any one or more of the aspects and / or methodologies of the present disclosure may be executed. It is also contemplated that multiple computing devices may be utilized to implement a specially configured set of instructions for causing one or more of the devices to perform any one or more of the aspects and / or methodologies of the present disclosure. The computer system 800 includes a processor 804 and a memory 808 that communicate with each other and with other components via a bus 812. The bus 812 may include any of several types of bus structures, including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combination thereof, using any of a variety of bus architectures.

[0101] Processor 804 may include any suitable processor, such as, but not limited to, a processor incorporating logic circuitry for performing arithmetic and logical operations, such as an arithmetic logic unit (ALU), which may be coordinated with a state machine and directed by operational input from memory and / or sensors. Processor 804 may be organized according to a Von Neumann and / or Harvard architecture, as non-limiting examples. Processor 804 may include, incorporate, and / or be integrated into, but is not limited to, a microcontroller, a microprocessor, a digital signal processor (DSP), a field programmable gate array (FPGA), a complex programmable logic device (CPLD), a graphical processing unit (GPU), a general purpose GPU, a tensor processing unit (TPU), an analog or mixed signal processor, a trusted platform module (TPM), a floating point unit (FPU), and / or a system on a chip (SoC).

[0102] Memory 808 may include a variety of components (e.g., machine-readable media), including but not limited to random access memory components, read-only components, and any combination thereof. In one example, a basic input / output system 816 (BIOS), containing the basic routines that help to transfer information between elements within computer system 800, such as during start-up, may be stored in memory 808. Memory 808 may also include instructions (e.g., software) 820 (e.g., stored on one or more machine-readable media) that embody any one or more of the aspects and / or methodologies of the present disclosure. In another example, memory 808 may further include any number of program modules, including but not limited to an operating system, one or more application programs, other program modules, program data, and any combination thereof.

[0103] Computer system 800 may also include storage 824. Examples of storage (e.g., storage 824) include, but are not limited to, hard disk drives, magnetic disk drives, optical disk drives in combination with optical media, solid-state memory devices, and any combination thereof. Storage 824 may be connected to bus 812 by an appropriate interface (not shown). Exemplary interfaces include, but are not limited to, SCSI, Advanced Technology Attachment (ATA), Serial ATA, Universal Serial Bus (USB), IEEE 1394 (FIREWIRE), and any combination thereof. In one example, storage 824 (or one or more components thereof) may be removably interfaced with computer system 800 (e.g., via an external port connector (not shown)). In particular, storage 824 and associated machine-readable media 828 may provide non-volatile and / or volatile storage of machine-readable instructions, data structures, program modules, and / or other data for computer system 800. In one example, software 820 may reside, in whole or in part, within machine-readable media 828. In another example, the software 820 may reside, completely or partially, within the processor 804 .

[0104] Computer system 800 may also include input device(s) 832. In one example, a user of computer system 800 may input commands and / or other information to computer system 800 via input device(s) 832. Examples of input device(s) 832 include, but are not limited to, an alphanumeric input device (e.g., a keyboard), a pointing device, a joystick, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), a cursor control device (e.g., a mouse), a touchpad, an optical scanner, a video capture device (e.g., a still camera, a video camera), a touch screen, and any combination thereof. Input device(s) 832 may interface to bus 812 via any of a variety of interfaces (not shown), including, but not limited to, a serial interface, a parallel interface, a gameport, a USB interface, a Firewire interface, a direct interface to bus 812, and any combination thereof. Input device(s) 832 may include a touch screen interface, which may be part of display 836 or may be separate, as described further below. The input device 832 may be utilized as a user selection device for selecting one or more graphical representations within the graphical interface, as described above.

[0105] A user may also input commands and / or other information to computer system 800 via storage device 824 (e.g., removable disk drive, flash drive, etc.) and / or network interface device 840. A network interface device such as network interface device 840 may be utilized to connect computer system 800 to one or more of a variety of networks, such as network 844, and one or more remote devices 848 connected thereto. Examples of network interface devices 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 networks 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, building, 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 combination thereof. A network such as network 844 may use wired and / or wireless communication modes. In general, any network topology may be used. Information (eg, data, software 820 , etc.) can be communicated to and from computer system 800 via network interface device 840 .

[0106] The computer system 800 may further include a video display adapter 852 for communicating displayable images to a display device, such as a display device 836. Examples of display devices include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combination thereof. The display adapter 852 and the display device 836 may be utilized in combination with the processor 804 to provide graphical representations of aspects of the present disclosure. In addition to a display device, the computer system 800 may include one or more other peripheral output devices, including, but not limited to, audio speakers, a printer, and any combination thereof. Such peripheral output devices may be connected to the bus 812 via a peripheral interface 856. Examples of peripheral interfaces include, but are not limited to, a serial port, a USB connection, a Firewire connection, a parallel connection, and any combination thereof.

[0107] The above was a detailed description of exemplary embodiments of the present invention. Various modifications and additions can be made without departing from the spirit and scope of the present invention. Each feature of the various embodiments described above can be combined with features of other described embodiments as necessary to provide multiple feature combinations in related new embodiments. Moreover, while the above describes several separate embodiments, what is described herein is merely illustrative of the application of the principles of the present invention. Furthermore, although certain methods herein may be illustrated and / or described as being performed in a particular order, the order is highly variable within the skill of the art to accomplish the methods, systems, and software according to the present disclosure. Thus, this description is intended to be taken only as an example, and not otherwise limiting of the scope of the present invention.

[0108] Exemplary embodiments have been disclosed above and illustrated in the accompanying drawings. Those skilled in the art will appreciate that various modifications, omissions, and additions can be made to what is specifically disclosed herein without departing from the spirit and scope of the present invention.

Claims

1. 1. An apparatus for generating a diagnostic label, said apparatus comprising: At least one processor; a memory communicatively coupled to the at least one processor, the memory comprising: receiving a plurality of electrocardiogram signals from a user; receiving a plurality of electronic health records from the user, the plurality of electronic health records including a plurality of metadata; generating a plurality of structured electronic health records using said plurality of electronic health records; using a representational machine learning model to generate a plurality of representations as a function of the plurality of electrocardiogram signals and the plurality of structured electronic health records, generating the plurality of representations; training the representational machine learning model using representational training data; and generating the plurality of representations as a function of the plurality of electrocardiogram signals and the plurality of structured electronic health records using the trained representation machine learning model; generating a diagnostic label as a function of said plurality of representations; a memory including instructions for configuring the at least one processor to display the diagnostic label using a display device; and An apparatus comprising:

2. The apparatus of claim 1 , wherein the plurality of representations includes a first representation.

3. The apparatus of claim 1 , wherein the plurality of representations includes a second representation.

4. The apparatus of claim 1 , wherein the plurality of representations includes a third representation.

5. The apparatus of claim 1 , wherein the plurality of metadata comprises a plurality of textual data.

6. The apparatus of claim 1 , wherein the plurality of electronic medical records includes a plurality of health data.

7. The apparatus of claim 6 , wherein generating the plurality of structured electronic health records comprises classifying the plurality of electronic health records into a plurality of diagnostic codes.

8. The apparatus of claim 1 , wherein generating the plurality of structured electronic health records comprises generating the plurality of structured electronic health records using a structural classifier.

9. The memory may include: generating a plurality of graphical data as a function of said plurality of representations; The apparatus of claim 1 , further instructed to identify one or more representational clusters as a function of the plurality of graphical data.

10. The apparatus of claim 1 , wherein the memory further instructs the processor to generate a diagnostic report as a function of the diagnostic label.

11. 1. A method for generating a diagnostic label, comprising: receiving, using at least one processor, a plurality of electrocardiogram signals from a user; receiving, using the at least one processor, a plurality of electronic health records from the user, the plurality of electronic health records including a plurality of metadata; generating, using the at least one processor, a plurality of structured electronic health records using the plurality of electronic health records; using the at least one processor to generate a plurality of representations as a function of the plurality of electrocardiogram signals and the plurality of structured electronic health records using a representation machine learning model, and generating the plurality of representations; training the representational machine learning model using representational training data; and generating the plurality of representations as a function of the plurality of electrocardiogram signals and the plurality of structured electronic health records using the trained representation machine learning model; generating, using the at least one processor, a diagnostic label as a function of the plurality of representations; and using the at least one processor, displaying the diagnostic label using a display device.

12. The method of claim 11 , wherein the plurality of representations includes a first representation.

13. The method of claim 11 , wherein the plurality of representations includes a second representation.

14. The method of claim 11 , wherein the plurality of representations includes a third representation.

15. The method of claim 11 , wherein the plurality of metadata comprises a plurality of textual data.

16. The method of claim 11 , wherein the plurality of electronic medical records includes a plurality of health data.

17. 20. The method of claim 16, wherein generating the plurality of structured electronic health records comprises categorizing the plurality of health data into a plurality of diagnostic codes.

18. The method of claim 11 , wherein the method further comprises using the at least one processor to generate the plurality of structured electronic health records using a structural classifier.

19. The method further comprising: generating, using said at least one processor, a plurality of graphical data as a function of said plurality of representations; The method of claim 11 , further comprising: using the at least one processor to identify one or more representational clusters as a function of the plurality of graphical data.

20. The method of claim 11 , wherein the method further comprises generating, using the at least one processor, a diagnostic report as a function of the diagnostic label.

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