Method of predicting a response of a subject to food is disclosed. The method comprises: selecting a food to which a response of the subject is unknown; accessing a first database having data describing the subject but not a response of the subject to the selected food; accessing a second database having data pertaining to responses of other subjects to foods, the responses of the other subjects including responses of at least one other subject to the selected food or a food similar to said selected food; and analyzing the databases based on the selected food to estimate the response of the subject to the selected food.

Method of predicting a response of a subject to food is disclosed. The method comprises: selecting a food to which a response of the subject is unknown; accessing a first database having data describing the subject but not a response of the subject to the selected food; accessing a second database having data pertaining to responses of other subjects to foods, the responses of the other subjects including responses of at least one other subject to the selected food or a food similar to said selected food; and analyzing the databases based on the selected food to estimate the response of the subject to the selected food.

A method and system for an autonomous multipurpose application for scheduling, check-in, and education is described herein. The autonomous multipurpose application may provide an improved experience for users that schedule appointments with people having specialties, and that check-in to the scheduled appointments. A cognitive intelligence platform may use artificial intelligence techniques to enable operations of the autonomous multipurpose application. Further, computing resources may be reduced using the described techniques.

A method and system for an autonomous multipurpose application for scheduling, check-in, and education is described herein. The autonomous multipurpose application may provide an improved experience for users that schedule appointments with people having specialties, and that check-in to the scheduled appointments. A cognitive intelligence platform may use artificial intelligence techniques to enable operations of the autonomous multipurpose application. Further, computing resources may be reduced using the described techniques.

Systems and methods for monitoring brain health include determining baseline health of a subject through clinical assessment. A first plurality of observables of the subject across a period of time, comprising a plurality of time intervals, is obtained. Also obtained is a second plurality of observables of the subject across the period. The first and second observables are each one of sensor measurements, measurements of one or more analytes, physiological measurements, and subjective observations. For each time interval, at one least observable in the first plurality of observables and in the second plurality of observables taken during the interval is used to compute a respective brain health index value set for the subject comprising two or more of a motor skill, cognitive skill, mood, and sleep status. A brain health trajectory is determined through comparison of these value sets to the baseline health of the subject at each time interval.

Techniques for granular analysis of a knowledge graph are provided. A profile comprising a plurality of attributes is received, and a knowledge graph is analyzed to identify a plurality of therapies, based on the plurality of attributes. A document that is relevant to a first therapy of the plurality of therapies is identified, and a criterion stated in the document is determined. Further, an aggregate value is determined for the criterion, based on a plurality of participants associated with the document, wherein the first aggregate value represents attributes of the plurality of participants. A weight is generated for the document, based at least in part on the plurality of attributes and the aggregate value. A score is generated for the first therapy, based at least in part on the weight, and an optimal therapy is determined, from the plurality of therapies, based in part on the score.

Provided is a literature information service method using a single computer or a plurality of computers connected to each other via a network. The literature information service method includes: transmitting a first character string to a plurality of first servers connected respectively to a plurality of databases each including information of enzymes, and receiving a plurality of pieces of data obtained by searching the plurality of databases with the first character string; extracting a plurality of second character strings indicating information of enzymes from the plurality of pieces of data; generating a search expression using at least one of the plurality of extracted second character strings; and searching a literature database using the search expression to acquire search result data.

Systems and methods to automatically generate classifiers are provided. A labeled dataset is initially received. The dataset may be for a positive class, or may be a negative for a class, or a false positive class. N features that are predictive for the class (or false positive or the negative class) are identified. These features are combined within a classifier dictionary. Medical records received may be processed in order to be machine readable. Features within the medical records are identified and are compared against the dictionary of classifiers. Matches indicate classes within the medical record. The classifier dictionary may be periodically updated in response to insufficient classification accuracy, or when new data becomes available.

A method for using a health information exchange system which stores patient record data regarding a multiplicity of patients, to serve a first plurality of EMRs each interacting with an EMR community including a set of at least one EMR, the method comprising: for each individual EMR within the first plurality of EMRs, performing a computerized context interception process using a processor to intercept context from the individual EMR and to identify there within an event whereby a health provider using the individual EMR calls up an individual patient's record from said individual EMR; and responsive to identification of the event, using a computerized output device for providing patient record data, pertaining to the individual patient, to the health provider.

This disclosure relates to method and system for providing user-specific explanations for an output generated by an Artificial Neural Network (ANN) model. The method may include receiving a training dataset, and identifying one or more relevant features from the training dataset. The method may further include distributing the one or more relevant features into a plurality of groups. The plurality of groups may correspond to a plurality of levels of domain knowledge of users. The method may further include generating a plurality of vocabularies of explanations for an output generated by the ANN model for the training dataset corresponding to the plurality of groups, using the one or more relevant features.