Abstract: A system for facilitating interoperability among health care modules includes an interface configured to receive a first electronic record from a first health care module. The first electronic record has a first data structure. The system also includes a processor communicatively coupled to the interface. The processor is configured to analyze the first electronic record having the first data structure, and, based on the analysis, extract a portion of data from the first electronic record. The processor further creates a second electronic record using the portion of data from the first electronic record, where the second electronic record has a second data structure. The second data structure is configured to be compatible with a second health care module. The interface is further configured to transmit the second electronic record for display to the second health care module.

Abstract: We disclose computational models that alleviate the effects of human ascertainment biases in curated pathogenic non-coding variant databases by generating pathogenicity scores for variants occurring in the promoter regions (referred to herein as promoter single nucleotide variants (pSNVs)). We train deep learning networks (referred to herein as pathogenicity classifiers) using a semi-supervised approach to discriminate between a set of labeled benign variants and an unlabeled set of variants that were matched to remove biases.

Abstract: A classification method and system for medical conditions based on the concept of subtypes, which are classes of patients whose medical fact patterns as analyzed in an N-dimensional space places them closer to other patients belonging to the same subtype than to patients who belong to different subtypes and, who share similar likelihood of certain specified outcomes. A computer system processes patient data for a plurality of patients from a set of patients called a cohort. The computer system processes the patient data for the cohort to group patients into sub-cohorts of similar patients, i.e., each sub-cohort includes patients who have similar medical fact patterns in their patient data. Patients in different sub-cohorts generally, but not necessarily, have significant differences in their patient data. The computer system generates quantitative definitions, describing the patients in the sub-cohorts.

Abstract: A patient-trial matching system (100) includes a structuralizer (102) configured to convert input non-structured patient health data and input non-structured clinical trial eligibility criteria into structured patient health data and structured clinical trial eligibility criteria by organizing a content of the non-structured data as known data elements. The patient-trial matching system further includes a semantic matcher (122) configured to match the structured patient health data and the structured clinical trial eligibility criteria based on user input matching criteria and outputs matched results. The patient-trial matching system further includes a ranking engine (126) configured to rank the matched results using ranking criteria (128), which include ranking patients matched to a clinical trial of interest in response to matching to find a group of trial patients and ranking clinical trials matched to a particular patient in response to matching to find a clinical trial.

Abstract: Comprehensive molecular profiling provides a wealth of data concerning the molecular status of patient samples. Such data can be compared to patient response to treatments to identify biomarker signatures that predict response or non-response to such treatments. This approach has been applied to identify biomarker signatures that correlate with response of cancer patients to platinum-based chemotherapy. Described herein are data structures, data processing, and machine learning models to predict a probability of benefit of a treatment for a disease or disorder of a subject having a particular set of biomarkers, as well as an exemplary application of such a model to precision medicine, e.g., to methods for selecting a treatment based on a molecular profile, e.g., a treatment comprising platinum therapy.

Abstract: The present tools and methods for detecting, diagnosing, predicting, prognosticating, or treating a neurobehavioral phenotype in a subject. These tools and methods relates to a genotype and neurophenotype topography-based approach for analyzing brain neuroimaging and gene expression maps to identify drug targets associated with neurobehavioral phenotypes and, conversely, neurobehavioral phenotypes associated with potential drug targets, to develop rational design and application of pharmacological therapeutics for brain disorders, and to provide methods and tools for treatment of subjects in need of neurological therapy.

Abstract: A method for classifying multi-granularity breast cancer genes based on a double self-adaptive neighborhood radius includes large-scale gene locus data are read and normalized, and a data analysis is performed on the large-scale gene loci. An optimum value K is selected by adopting a combination of contour coefficients and a PCA dimensionality reduction visualization, and a model of information granulation is adjusted. A heuristic reduction algorithm is used to implement a multi-granularity attribute reduction of a self-adaptive neighborhood radius based on a cluster center distance and a multi-granularity attribute reduction of a neighborhood radius based on an attribute inclusion degree, and big data for breast cancer genes are classified and predicted by adopting a machine learning classification algorithm based on a SVM support vector machine.

Abstract: Systems and methods for assisting a user in discovering nearby medical services are disclosed. A method includes identifying a user based on matching of at least one unique identity or biometric details of the user with data stored in a database. Details of an event data may be received from the user. A current geographical location of the user may be determined. Relevant support network data may be identified based on the event data and the current geographical location of the user. The support network data may include data related to pharmacies, doctors, hospitals, clinics, third parties, insurance, and payment agents. Thereafter, the relevant support network data may be presented to the user in form of an event map. The event map may include details related to the event data and the user preferences.

Abstract: Methods, systems, and computer-readable media for generating a personalized action recommendation are provided. The method acquires a request for a service that is associated with a user and the user's condition. The method then identifies one or more features of the user based on stored user information. The method next assigns the user to a segment based on the identified one or more features, generates a set of one or more recommended actions for the user based on the segment, and determines an expected value of each of the one or more recommended actions. The method determines a rank of the one or more recommended actions based on the expected value of each of the one or more recommended actions, and outputs a recommended action with a highest expected value for the user in response to the request for the service.

Abstract: Systems and methods for conducting automated synthetic interactions with a user, such as a patient at home following a medical procedure. A digital coach having a processor and memory initiates a session with a user's interactive device, and presents pre-recorded scripts as video and/or audio through the interactive device. The user's responses are received by the digital coach through the interactive device. Peripheral devices, such as medical devices, may be used by the user or controlled by the digital coach to obtain data measurements regarding the physiological condition of the user. The processor of the digital coach analyzes the data from the user responses and devices, and semantically interprets the responses and data to determine the next action and script to present the user in the session. The digital coach provides a conversational, dynamic, adaptive session with a user based on semantically expanded interpretations of data by the processor.

Abstract: Mechanisms are provided for determining values to associate with medical conditions of a patient. A patient assessment is received that comprises a natural language question and a corresponding answer, about a patient, provided in response to the question. Cognitive natural language processing is performed on the patient assessment to extract features from the natural language question and corresponding answer. The extracted features are evaluated within a context of a pre-existing electronic medical record of the patient. A value for a medical condition of the patient is determined based on results of the evaluation and stored in the electronic medical record for the patient.

Abstract: Systems and methods for generating user-specific recommendations of products or services by collaborative filtering executed and/or performed by one or more trained or untrained predictive models configured to ingest product attribute(s), product purpose(s), user location data, and/or user demographics. The predictive model(s) are leveraged to detect and determine user-specific preferences for, and preferences against, particular attributes, features, ingredients, aesthetic styles, and so on.

Abstract: An apparatus and method for determining a composition of a replacement therapy treatment is presented, the apparatus at least a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to receive a user input wherein the user input comprises at least an identifier and a constitutional history of the user, generate a first condition descriptor as a function of the user input, determine a composition of a replacement therapy treatment as a function of the first condition descriptor, wherein the determination comprises training a first machine-learning process using user training data, wherein the user training data correlates user inputs to compositions of the replacement therapy treatment and determining the composition as a function of the user input and the first machine learning process, and output the composition of the replacement therapy treatment as a function of the determination.

Abstract: According to one embodiment, a medical information processing apparatus includes at least one processor. The processor is configured to execute a program to acquire first data collected from a test object, input the acquired first data into a first model, determine whether second data, which is output from the first model receiving the first data, is to be input into a second model, and in a case where the processor determines that the second data is to be input into the second model, input the second data into the second model, and output third data which is output from the second model receiving the second data.

Abstract: A system for causative chaining of prognostic label classifications includes a classification device configured to receive training data including a plurality of first data entries, each including at least a first element of physiological state data and at least a correlated first prognostic label and a plurality of second data entries, each including at least a second prognostic label and at least a correlated third prognostic label, and to record at least a first biological extraction. The system includes a prognostic label learner configured to generate at least a first prognostic output as a function of the first training set and the at least a physiological test sample, and a causal link learner configured to generate at least a second prognostic output causally linked to the first prognostic output as a function of the second training set and the at least a first prognostic output.

Abstract: A peer-review flagging system is operable to train a computer vision model and to generate automated assessment data by performing an inference function on a first medical scan by utilizing the computer vision model. Human assessment data is generated based on a first medical report written by a medical professional in conjunction with review of the first medical scan. First consensus data is generated based on the automated assessment data, the human assessment data, and a first threshold, and the first medical scan is determined to be flagged based on the first consensus data. A second threshold is selected use in generating second consensus data for a second medical scan and a second medical report written by the medical professional in conjunction with review of the second medical scan, and is selected to be stricter than the first threshold based on determining to flag the first medical scan.

Abstract: An apparatus and method for generating a treatment plan for salutogenesis, the apparatus comprising a at least a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to receive physiological data associated with a user and comprising a plurality of biomarkers, wherein the plurality of biomarkers comprise at least a glycocalyx degradation biomarker, determine a concentration for each at least a glycocalyx degradation biomarker of the plurality of biomarkers, classify the at least a glycocalyx degradation biomarker to a disease condition and a treatment label as a function of the concentration, and generate a treatment plan as a function of the disease condition and the treatment label.

Abstract: There is a need for more effective and efficient predictive data analysis solutions. This need can be addressed by, for example, obtaining prediction input objects each associated with a predictive entity; performing iterations of an iterative cohort generation routine until a qualified predictive model is identified, wherein each iteration comprises determining one or more predictive cohorts for predictive entities based on the prediction input objects, generating a predictive model based on the predictive cohorts, performing a predictive inference based on the predictive model to generate a current iteration prediction, generating a predictive score based on the current iteration prediction, and determining whether the predictive model is the qualified predictive model based on whether the predictive score exceeds a predictive score threshold; and performing cohort-based predictive data analysis based on the qualified predictive model to generate a respective final prediction for each predictive entity.

Abstract: The present disclosure provides a method and system for providing medical services to a user. The method may include acquiring first information of a medical process including a plurality of sub-processes to be allocated with medical resources. The method may also include obtaining second information on medical resources, and the second information may include available time slots and locations of the medical resources. The method may further include allocating the medical resources for the medical process based on the first information and the second information. The method may also include determining, for at least one sub-process of the plurality of sub-processes of the medical process, appointment information of the at least one sub-process based on at least a portion of the second information corresponding to the medical resource allocated to the at least one sub-process.

Abstract: A system for classifying patient parameter values may include at least one processor programmed to access first information associated with a plurality of patients, the first information including a plurality of patient parameters associated with the plurality of patients, the first information being accessed electronically via a database; determine a first value associated with a patient parameter of at least one of the plurality of patients; analyze second information associated with at least one patient to determine a second value of the patient parameter; detect, based on analysis of at least the first value and the second value, a potential anomaly in the second value; and cause a graphical user interface of a computing device to display at least one graphical element indicating the potential anomaly.