539 results about "Clinical information" patented technology

The invention relates to the technical field of data processing, and discloses a preoperative risk comprehensive assessment method and system for breast cancer and a storage medium. The method comprises the following steps: collecting clinical information and carrying out digital processing; segmenting the multi-modal image by using deep learning; analyzing a pathological section based on the image features; carrying out multi-level fusion modeling to obtain a risk heat map; performing digital twinborn simulation in combination with a knowledge graph; and dynamically processing the follow-up data to form a risk adjustment mechanism. According to the method, accurate quantification and individualized treatment decision making of the preoperative risk of the breast cancer patient are realized, and the prediction accuracy is improved.

The invention discloses a mild traumatic brain injury image evaluation system based on deep learning, and relates to the technical field of medical imaging, and the system comprises a data source module which is used for obtaining head magnetic resonance imaging data as an input data source; the preprocessing module is used for carrying out de-noising processing and image registration on the skull magnetic resonance imaging data to realize standardized preprocessing; and the feature extraction module is used for extracting a local feature map from the multi-dimensional image data by constructing a multilayer convolutional neural network, and identifying a subcortical structure change and damage suspicious region. According to the mild traumatic brain injury image evaluation system based on deep learning provided by the invention, a standardized evaluation report automatically generated by the system not only integrates clinical information of a patient, but also provides detailed injury types, severity and follow-up visit suggestions, so that the efficiency and reliability of clinical decision making are greatly improved; and powerful support is provided for formulating an individualized diagnosis and treatment scheme.

The invention discloses an intestinal cancer immune response graph neural network prediction method, a medium and equipment, and the method comprises the steps: collecting pathological image information, immunodetection information and basic clinical information, extracting a tissue space distribution characteristic spectrum through a deep convolutional network, and constructing a graph neural network model in combination with an immunomarker expression characteristic matrix; spatial interaction characteristics of a tumor microenvironment are modeled by adopting a graph attention mechanism, finally a treatment response probability, an optimal treatment opportunity and an adverse reaction risk are predicted through a multi-task learning framework, and a clinical decision report containing a prediction response curve, a risk early warning threshold and a treatment time window suggestion is output. According to the method, through multi-modal data fusion and spatial interaction modeling, accurate prediction of intestinal cancer immunotherapy response is realized, and a more comprehensive reference basis is provided for clinical decision making.

The invention provides a prognosis risk prediction model construction method based on coexistence of acute myocardial infarction and atrial fibrillation and application. The method comprises the following steps: S1, acquiring historical patient clinical information of a target patient and death time after coexistence of diseases; s2, preprocessing clinical information of historical patients; s3, using an LASSO method and a ten-fold cross validation method to identify related clinical features related to death time after coexistence of diseases; s4, dividing a training set and a verification set; s5, performing training optimization on the four machine models by using the training set, evaluating the prediction performance of the models by using the verification set, and determining a Glmnet model with the optimal prediction performance; and S6, performing model interpretation on the Glmnet model by using an SHAP value, and determining an optimal prediction factor of which the model prediction contribution ranks the first three. The prognosis risk of coexistence of acute myocardial infarction and atrial fibrillation can be accurately predicted, and reference is provided for clinical diagnosis and treatment of patients with coexistence of acute myocardial infarction and atrial fibrillation.

The invention provides a clinical auxiliary decision-making method based on artificial intelligence and a related product, and the method comprises the steps: building a knowledge graph covering medical entities and relationships through learning authoritative medical data and medical records; a doctor inputs patient information, and data standardization and cleaning are carried out; based on the multi-dimensional information of the patient, recommending suspected diagnosis and differential diagnosis; recommending a preset examination scheme according to the suspected diagnosis and differential diagnosis results; when a doctor's advice is issued, the system checks conflicts between inspection and inspection items in real time and provides risk prompts; similar medical records are recommended based on patient clinical information, and diagnosis and treatment reference is provided for doctors. The clinical aid decision-making method based on artificial intelligence and related products have the beneficial effects that the disease list of suspected diagnosis and definite diagnosis is generated by automatically analyzing the illness state of the patient, and examination, inspection and treatment schemes are recommended in a targeted manner, so that the problems of missed diagnosis and misdiagnosis are effectively avoided.

The invention discloses a patient clinical information management system and method, and relates to the technical field of medical information management and data security, and the system comprises a data collection module which is used for receiving patient basic information and diagnosis and treatment records; the encryption storage module is used for establishing a patient file identification number according to the basic information of the patient and the diagnosis and treatment record, encrypting the basic information of the patient and the diagnosis and treatment record and then storing the encrypted basic information and the encrypted diagnosis and treatment record into a corresponding electronic medical record database; the semantic analysis module is used for performing semantic analysis on the diagnosis and treatment records and extracting clinical feature tags; the clinical feature construction module is used for constructing a patient similarity matrix based on the clinical feature tags obtained by the semantic analysis module, and performing clustering analysis to obtain a clinical feature group; and the treatment template generation module is used for constructing a corresponding treatment scheme template for each clinical feature group. According to the invention, the safety, accuracy and intelligent level of patient information management are significantly improved.

The invention discloses a prostate cancer three-classification risk layering method based on an integrated learning model, and relates to the technical field of data processing and analysis. The method comprises the following steps: collecting clinical information and pathological data of a patient with increased PSA, and dividing the data into a training set and a test set; the training set is preprocessed, and prediction features are screened through LASSO regression; constructing a plurality of machine learning base models based on the features, and training and optimizing through cross validation; soft voting is constructed through an integration strategy, and an integration model is stacked; setting double thresholds according to the integrated model prediction probability, and establishing a layering rule; combining an integrated model and rules to form a three-classification model, and judging low, high and medium risks according to probabilities; and finally verifying the model diagnosis performance in the test set. According to the method, through cross-modal feature integration and ensemble learning, the method is PSAlt; accurate risk stratification is provided for 30 ng / mL people, biopsy decision-making efficiency is optimized, and excessive puncture and missed diagnosis risks are reduced.

The invention discloses an intelligent diagnosis and risk assessment method and system for cerebral apoplexy related to atrial fibrillation, and relates to the technical field of atrial fibrillation detection.The method comprises the steps that multi-dimensional data of atrial fibrillation patients in a clinical information system are integrated, a structured database is generated, and a standardized data set is output; a standardized data set is adopted to train a first machine learning model, and model performance is optimized through parameter joint search and a training set-verification set convergence dynamic monitoring mechanism; performing cross validation on a feature weight sorting result in the optimized diagnosis model and a clinical index risk association degree calculated by a second machine learning model to generate an interaction map; and based on clinical event data containing timestamps in the structured database, adopting a third machine learning model to extract time sequence features, and combining with a survival probability analysis model to generate a risk assessment report. According to the invention, through an intelligent model adjusting and optimizing mechanism, multi-dimensional medical data are effectively integrated, and the recognition precision of the atrial fibrillation related cerebral apoplexy is greatly improved.

The invention relates to a nitric oxide data analysis method and system for bronchial asthma assessment. According to the method, exhaled air nitric oxide and pulmonary alveolar nitric oxide of a patient, omics data and clinical information are acquired in a standardized manner, and multi-dimensional features are constructed after data cleaning and fusion; comprise an inflammation level index, an inflammation region entropy, an inflammation synergy index, a glucocorticoid response factor, an infection-smoking synergy influence factor and a heredity-symptom distribution index. And outputting initial risk assessment based on an explainable elevator EBM model, and finally generating an asthma risk probability through a weighted integration formula to realize three-level layering: low / medium / high risk. The system dynamically associates treatment decisions, for example, the drug dosage and the monitoring frequency are improved when the risk is upgraded, and the medication scheme is optimized when the risk is degraded. According to the scheme, the limitation of traditional single marker static analysis is broken through, multiple mechanisms of dissection, immunity and pharmacology are fused, the evaluation precision and clinical applicability are remarkably improved, the acute attack rate is reduced, and accurate typing treatment is guided.

Various systems and methods for generating a health outcome prediction for a patient are disclosed herein. The embodiments described involve operating a processor to receive a health condition of interest, receive a health dataset including patient data associated with the patient that includes demographic information about the patient, and clinical information about the patient. At least a portion of the health dataset is unstructured data and the health dataset originates from distinct data sources. The described embodiments then involve operating the processor to process the unstructured data to obtain structured data, extracting from the processed health dataset, features determined to be relevant to the health condition of interest, determine a risk score for the patient for the health condition of interest using a trained health prediction engine, based on the features extracted and generate and display a graphical output based on the risk score.

The invention provides a prostate cancer biochemical recurrence prediction system based on a deep learning image and clinical information multi-mode model. The prostate cancer biochemical recurrence prediction system comprises an input module, a preprocessing module, a feature selection module, a fusion module, a prediction module, a data management and storage module and a result output and decision support module. According to the method, bpMRI image data and clinical information of a patient are fully utilized, multi-mode information is fully fused through a deep learning technology, and accurate prediction of biochemical recurrence is achieved. And the robustness of the model is further enhanced by adopting 5-fold cross validation and a diversified data enhancement strategy. Experimental results show that the method can assist in making more accurate patient layering and personalized follow-up visit schemes, and potentially reduces the over-treatment and missed diagnosis risks. The invention provides an efficient and reliable AI solution for prostatic cancer prognosis evaluation, and provides powerful support for promoting precise medical treatment and making personalized treatment strategies.

The invention relates to the technical field of orthodontics, and discloses a processing system and a processing method for a bracket-free invisible orthodontic tooth socket, and the processing system comprises a data collection and integration module which is used for collecting and integrating multi-source data, comprising collection approaches and specific operation processes of oral three-dimensional scanning data, basic information of a patient, clinical examination records, correction scheme records and correction result evaluation; the data standardization and cleaning module is used for carrying out format unification and cleaning on the collected data; and the feature extraction and labeling module is used for extracting tooth form and tooth space relation features from the oral cavity three-dimensional scanning data and labeling the malocclusion and occlusion relation in the oral cavity. The invisible orthodontic tooth socket is manufactured by collecting a large amount of multi-dimensional data including oral three-dimensional scanning data, patient clinical information, orthodontic process records, result evaluation and the like, and the moving path of each tooth in each orthodontic stage and the magnitude and direction of required orthodontic force are accurately predicted.

The invention discloses a treatment response prediction method and system, a storage medium and a terminal, and the method comprises the steps: carrying out the heterogeneity analysis and cell division of first single cell sequencing data of a patient through employing a clustering algorithm, and obtaining a plurality of corresponding cell subgroups; performing feature extraction and biomarker recognition on all the cell subgroups to obtain corresponding cell subgroup features; wherein the cell subset characteristics comprise a cell development trajectory and a plurality of cell markers; and inputting the clinical information and the cell subset characteristics of the patient into the treatment response prediction model, so that the model performs multi-factor analysis and outputs a treatment response prediction result of the patient for each treatment scheme. By adopting the embodiment of the invention, the tiny key difference between the cells is captured, the cells are divided based on the difference between the cells, and then feature extraction and biomarker recognition are performed on each cell subset, so that the specific gene expression modes of different cell subpopulations corresponding to diseases can be recognized, and the treatment response prediction accuracy is further improved.

The invention discloses a venous thrombosis risk assessment method and system based on a large language model and deep learning, and the method and system can comprehensively capture the clinical information of a patient through the comprehensive utilization of multi-dimensional and multi-modal clinical diagnosis and treatment data, and improve the accuracy of risk assessment. An automatic feature extraction and selection technology is realized by applying a large language model, manual intervention is reduced, and the comprehensiveness and accuracy of feature extraction are improved. The introduction of the deep learning model enhances the prediction performance of the model, supports real-time data processing and risk prediction, and realizes the automation and real-time performance of risk assessment. Besides, the model framework supports continuous learning and updating, can adapt to new clinical data and research progress, and keeps advancement and effectiveness of the model, thereby providing high-quality hospitalized patient VTE risk assessment services.

The invention provides a traditional Chinese medicine electronic medical record natural language processing and knowledge graph construction method and system. The method belongs to the technical field of the crossing field of traditional Chinese medicine informatization, artificial intelligence natural language processing and knowledge engineering, and comprises the following steps: performing multi-granularity semantic unit division on the traditional Chinese medicine electronic medical record to generate traditional Chinese medicine text semantic unit data; constructing a multi-granularity semantic decoupling engine according to the traditional Chinese medicine text semantic unit data so as to construct a traditional Chinese medicine text semantic analysis framework; performing traditional Chinese medicine unstructured text deep analysis according to the traditional Chinese medicine text semantic analysis framework, and performing multi-dimensional semantic feature extraction to obtain traditional Chinese medicine text semantic feature data and medical record time sequence data; through multi-granularity semantic unit division and deep analysis, the unstructured text in the traditional Chinese medicine electronic medical record can be effectively converted into structured semantic data, and the ability to understand and process traditional Chinese medicine clinical information is further improved.

Embodiments herein provide a system and method for screening and monitoring of cardiac diseases by analyzing acquired physiological signals. Unlike state of art approaches that consider only synchronized ECG and PPG signals for cardiac health analysis and do not consider PCG which is a critical signal for CAD analysis, the system synchronously captures physiological signals such as photo plethysmograph (PPG), phonocardiogram (PCG) and electrocardiogram (ECG) from subject(s) and builds an analytical model in the cloud for analyzing heart conditions from the captured physiological signals. The system and method provides a fusion based approach of combining the captured physiological signals such as PPG, PCG and ECG along with other details such as subject clinical information, demography information and so on. The analytical model is pretrained using ECG. PPG and PCG along with metadata associated with the subject such as demography and clinical information.

The invention belongs to the technical field of intelligent diagnosis, and particularly relates to an AI combined MRI and clinical JIA diagnosis system and a storage medium. According to the system disclosed by the invention, the early auxiliary diagnosis of the juvenile idiopathic arthritis is carried out by combining artificial intelligence with multi-dimensional and multi-modal information of multi-sequence MRI images of knee joints of children and various clinical information. The main technology of the method is child knee joint tissue segmentation based on deep learning, a multi-dimensional feature extraction strategy based on a segmentation result, and disease classification based on multi-modal feature integration and deep learning. By integrating the multi-dimensional features of the multi-sequence MRI images and fusing different modal features such as image information and clinical information, an auxiliary diagnosis result with high accuracy can be provided. The technology provided by the invention is beneficial to the realization of early diagnosis and early treatment of juvenile idiopathic arthritis, and has a very good application prospect.

The present disclosure includes methods and devices used to diagnose or treat or manage bone conditions such as osteoporosis. The present disclosure includes bone and soft tissue imaging methods, some of which involve specific patient positioning and alignment of bodily regions and ensuring consistent imaging at multiple time points. Several artificial intelligence-based techniques are also disclosed that process imaging data and other clinical inputs using machine learning models and other methods to obtain desired clinical information. The disclosed artificial intelligence-based techniques are designed to use imaging data and other clinical inputs generated by the imaging methods and devices of the disclosure or existing imaging data and other clinical inputs.

The invention discloses a case anomaly detection method and system based on deep learning, and relates to the technical field of medical images. Comprising a data collection and preprocessing module, a multi-modal feature extraction module, a feature fusion module, a context information integration module, an anomaly detection model module, a model training and optimization module, a model iteration training and verification module and a real-time monitoring and feedback module. The method further comprises the following steps: step 1, collecting medical image data from different modalities, and carrying out denoising and standardization processing on the medical image data; 2, extracting features of different modal images by using a deep convolutional neural network, and fusing the features to form a comprehensive feature vector; 3, combining the clinical information of the patient with the fusion features to generate a comprehensive feature vector; 4, analyzing the fused feature vector through the trained deep learning model, and outputting an anomaly detection result; 5, the system monitors a new case in real time, automatically prompts abnormity and continuously updates the model according to feedback of a doctor.

The invention discloses an intelligent tuberculosis screening and data analysis system, relates to the technical field of medical data analysis systems, and aims to improve the diagnosis efficiency and accuracy of tuberculosis. The technical key points are that the system comprises a data acquisition module, a preprocessing module, an AI diagnosis module, a multi-modal data fusion module, a data analysis module, an intelligent follow-up visit module and a database management module. By collecting image data and clinical information of a patient, the system adopts a deep learning algorithm to automatically analyze an image, identifies a potential tuberculosis focus, and provides a personalized diagnosis result in combination with a multi-modal data fusion technology. Meanwhile, the system has intelligent follow-up visit and remote monitoring functions, physiological indexes of the patient are obtained in real time through the wearable device, and dynamic illness state tracking and early warning are carried out. According to the method, the accuracy and efficiency of tuberculosis screening can be remarkably improved, precise medical services are provided for patients, and epidemiological prediction and prevention and control in the public health field are supported.

The invention relates to the field of medical image analysis, in particular to a lung adenocarcinoma EGFR gene mutation detection system and method based on PET / CT deep learning, and the system comprises a data collection module which is used for obtaining PET / CT image data and clinical information of a lung adenocarcinoma patient; the image preprocessing module is connected with the data acquisition module and is used for carrying out standardization processing and ROI extraction on the PET / CT image data; the feature extraction module is connected with the image preprocessing module and used for extracting depth features, metabolic parameter features and CT sign features from the PET / CT image data; the multi-modal data fusion module is connected with the feature extraction module and used for fusing the extracted multi-modal features; and the prediction model module is connected with the multi-modal data fusion module and is used for predicting the lung adenocarcinoma EGFR gene mutation state and prognosis based on the fusion features, and the prediction accuracy is improved through multi-modal data fusion and deep learning technologies.

The invention relates to the technical field of ultrasound contrast, in particular to an ultrasound contrast T-tube sinus tract recognition method based on deep learning. The method comprises the following steps: firstly, collecting contrast agent parameters, ultrasonic equipment parameters and clinical information, adjusting a prediction model by utilizing the contrast agent parameters, and optimizing the contrast agent parameters in combination with the clinical information; and then, acquiring an ultrasonic contrast image of the T-tube sinus tract according to the updated contrast agent parameters and the ultrasonic equipment parameters. Thirdly, preprocessing the T-tube sinus tract ultrasonic contrast image, inputting the preprocessed T-tube sinus tract ultrasonic contrast image into the multi-task ultrasonic contrast image enhancement model, and performing image enhancement and adaptive adjustment in combination with ultrasonic equipment parameters; and finally, constructing an ultrasound contrast image multi-stage classification identification model, identifying the T-tube sinus tract ultrasound contrast enhanced image to obtain a T-tube sinus tract identification result, and combining clinical information to assist medical personnel in diagnosis. According to the invention, the T-tube sinus tract identification precision and accuracy can be improved.

The invention discloses an electrocardiogram analysis method and device based on a deep learning model and a medium, and relates to the field of deep learning, and the method comprises the steps: carrying out the preprocessing of an electrocardiogram, and carrying out the noise suppression; inputting the electrocardiosignals into a pre-trained deep learning model, extracting time features and spatial features, and performing cross-modal feature fusion; synchronously executing a plurality of anomaly detection tasks, and synchronously executing a time sequence prediction task; aiming at the abnormal detection result, correcting the abnormal detection result according to the clinical information and the time sequence prediction result; and outputting an analysis result corresponding to the electrocardiogram. A complete closed loop is formed from signal processing to feature extraction, multi-task analysis and result correction, manual intervention links are reduced, electrocardiogram analysis time is remarkably shortened, result consistency is guaranteed, end-to-end automation is achieved, and efficiency is improved.

The invention is applicable to the technical field of clinical medicine, provides a clinical multi-modal cancer drug response prediction method based on feature reconstruction, constructs a clinical multi-modal model for drug response prediction of diffuse large B-cell lymphoma, and aims to predict the drug response of diffuse large B-cell lymphoma by integrating gene sequencing and clinical multi-modal data. And accurate drug reaction prediction is realized. The model adopts an end-to-end multi-stage processing flow: firstly, extracting gene features through TransP-Net, and processing multi-modal clinical data by using a clinical information encoder; then, pseudo-gene features are generated through a clinical-genome filling module to deal with the data missing problem; and finally, multi-modal deep fusion is realized through a clinical information decoder, and a prediction result is output. According to the method, data characteristics and working processes in a real clinical environment are fully considered, two conditions of complete gene data and missing gene data can be processed at the same time, and the method has a good clinical transformation prospect and application value.

Provided in the present invention are a method for constructing a pre-eclampsia risk assessment prediction model, and a prediction product, a prediction system, and a related application of the method. Specifically, the method of the present invention comprises: constructing a logistic regression analysis model on the basis of acquired clinical indexes of a pregnant woman, and obtaining the prior probability of early-onset pre-eclampsia or late-onset pre-eclampsia in the pregnant woman; performing median-of-multiples calibration on an acquired MAP, IGFBP1 and / or PLGF of the pregnant woman, and respectively constructing a multivariate normal distribution model for a sick pregnant woman and a multivariate normal distribution model for a healthy pregnant woman on the basis of an MAP MoM value and an IGFBP1 MoM value and / or a PLGF MoM value, so as to obtain the conditional probability of early-onset pre-eclampsia or late-onset pre-eclampsia in the pregnant woman; and in view of the prior probability and the conditional probability, calculating the posterior probability of early-onset pre-eclampsia or late-onset pre-eclampsia in the pregnant woman on the basis of a Bayesian model. In the present invention, the detection of related protein markers and the obtaining of maternal clinical information data are relatively easy, the accuracy of predicting early-onset pre-eclampsia and late-onset pre-eclampsia is relatively high, and clinical popularization and application are facilitated; therefore, the present invention is of great value to the prevention and early diagnosis of a disease.

Methods for generating a report of genomic and medical information associated with a patient are described. The methods may comprise, for example, receiving genomic testing data associated with the patient. The methods may comprise, for example, based on the genomic testing data, retrieving medical information including one or more potential clinical treatments for the patient. The methods may comprise, for example, determining that the medical information has at least some clinical significance to the patient. The methods may comprise, for example, based on at least a portion of the medical information having at least some clinical significance to the patient, generating patient-specific medical data. The methods may comprise, for example, determining at least one specific position to dispose the medical information on the report based on the patient-specific medical data and generating the report based on the determined at least one specific position.

The invention discloses an intelligent nursing record generation method and device, and the method comprises the steps: obtaining nursing voice data, and carrying out the noise reduction and sound enhancement processing to obtain a preprocessed voice stream; a speech recognition technology fusing ECAPA-TDNN and an x vector is adopted to recognize medical terminologies, and a text transcription result is generated; recognizing the voice of the target nurse from the preprocessed voice stream and the transcription result in combination with a speaker-independent model and a speaker condition model to obtain a voice transcription text of the voice; performing voice analysis and structured processing on the text by using a multi-modal self-supervised learning model and a hierarchical CNN-BiLSTM framework to obtain a structured text with clinical semantic features; key clinical information is extracted in combination with the medical ontology knowledge base, and a nursing record element set is obtained; and based on the nursing record element set, automatically generating a nursing record meeting the specification. According to the invention, the automatic generation from the nursing voice to the standardized nursing record is realized, and the efficiency and accuracy of the medical nursing record are improved.

The invention provides a premature delivery prediction method and device, electronic equipment and a readable storage medium, and relates to the technical field of data analysis. The method comprises the following steps: based on a preset model, scoring acquired comprehensive information of a pregnant woman in an early pregnancy stage; wherein the comprehensive information comprises demographic information, clinical information and biochemical index information; and predicting the premature delivery risk of the pregnant woman according to the scoring result. According to the method, the comprehensive information of the pregnant women at the early pregnancy stage is scored through the preset model, the premature delivery risk of the pregnant women is predicted according to the scoring result, quantitative evaluation of premature delivery risk prediction of the pregnant women at the early pregnancy stage is achieved, and objectivity and certain accuracy are achieved; and the difficulty and the cost of obtaining the comprehensive information of the pregnant women in the early pregnancy are relatively low, the difficulty and the cost of predicting the premature delivery risk of the pregnant women in the early pregnancy are reduced, and medical resources are saved.

The invention discloses a breast cancer lymph node metastasis prediction system based on a gene spectrum, and relates to the technical field of breast cancer prediction systems. Comprising a data acquisition module which collects gene spectrum data of a breast cancer patient and collects detailed clinical information of the patient; the preprocessing module is used for carrying out data cleaning and data normalization processing on the collected data; and the feature extraction module is used for extracting principal component features by applying principal component analysis on the basis of the gene expression data. According to the method, gene expression data are considered, various gene spectrum data such as gene mutation and copy number variation and detailed clinical information are integrated, the biological characteristics of the breast cancer can be reflected more comprehensively, and the prediction accuracy is improved.

The invention relates to a method for acquiring a biomarker for assisting CRLM early diagnosis. The method comprises the following steps: acquiring sequencing data, clinical information and metabonomics characteristics of a CRC sample; evaluating and determining a plurality of differentiated machine learning models; performing classification according to the determined machine learning model, and constructing a CRLM biomarker prediction model; screening candidate biomarkers according to the prediction model; and verifying the candidate biomarker at least according to the tissue slice and the serum sample, and determining a final biomarker.