Provided are moving image obtaining means that obtains a moving image of a region including at least a mouth or a peripheral portion of the mouth in a face, analysis means that analyzes a chewing action based on the moving image of the region obtained by the moving image obtaining means, quality determination means that determines quality of the chewing action based on information of the chewing action analyzed by the analysis means, and extraction means that extracts assistance information corresponding to the chewing quality determined by the quality determination means, from chewing information storage means.

A disease prediction method, apparatus, and computer program are provided. A disease prediction method according to several embodiments of the present disclosure can comprise the steps of: constructing a disease prediction model by learning learning data including ribosome data and disease information for learning, acquiring test ribosome data of an examinee; and predicting disease information about the examinee form the test ribosome data by using the disease prediction model. The disease prediction model can accurately predict disease information about the examinee by detecting and learning the characteristics of ribosome data, which vary according to disease information.

A method includes causing at least a portion of a knowledge graph representing ontological health related information to be presented on a display of a client device. The method further includes receiving, at an artificial intelligence engine, a medical query, wherein the medical query includes a plurality of strings of characters. The method further includes identifying, in the plurality of strings of characters, indicia comprising a phrase, a predicate, a keyword, a subject, an object, a cardinal, a number, a concept, or some combination thereof. The method further includes comparing the indicia to the knowledge graph to generate an answer responsive to the medical query. The method further includes causing the answer to be presented at the client device.

A method includes causing at least a portion of a knowledge graph representing ontological health related information to be presented on a display of a client device. The method further includes receiving, at an artificial intelligence engine, a medical query, wherein the medical query includes a plurality of strings of characters. The method further includes identifying, in the plurality of strings of characters, indicia comprising a phrase, a predicate, a keyword, a subject, an object, a cardinal, a number, a concept, or some combination thereof. The method further includes comparing the indicia to the knowledge graph to generate an answer responsive to the medical query. The method further includes causing the answer to be presented at the client device.

A computer implemented method and apparatus determines a body region represented by medical imaging data stored in a first image file. The first image file further stores one or more attributes each having an attribute value comprising a text string indicating content of the medical imaging data. One or more of the text strings of the first image file are obtained and input into a trained machine learning model, the machine learning model having been trained to output a body region based on an input of one or more such text strings. The output from the trained machine learning model is obtained thereby to determine the body region represented by the medical imaging data. Also disclosed are methods of selecting one or more sets of second medical imaging data as relevant to first medical imaging data.

Provided is a real-time multi-monitoring method using an electrocardiograph and a real-time multi-monitoring method using an electrocardiograph including connecting at least one of a plurality of electrocardiographs via a network; receiving identification information of a user which is linked to an electrocardiograph to identify the plurality of electrocardiographs; collecting biometric signal information from the plurality of electrocardiographs; and analyzing a health condition in consideration of predetermined reference information which is defined for each user and biometric signal information collected from each of the plurality of electrocardiographs.

A method including generating, by a state engine from data describing behaviors of users in an environment external to the state engine, an executable process. An agent executes the executable process by determining, from the data describing the behaviors of the users, a problem of at least some of the users, and selects, based on the problem, a chosen action to alter the problem. At a first time, a first electronic communication describing the chosen action to the at least some of the users is transmitted. Ongoing data describing ongoing behaviors of the users is monitored. A reward is generated based on the ongoing data to change a parameter of the agent. The parameter of the agent is changed to generate a modified agent. The modified agent executes the executable process to select a modified action. At a second time, a second electronic communication describing the modified action is transmitted.

A brain stimulation simulation system and method according to a preset guide system using an anonymized data-based external server are provided. According to various embodiments of the present invention, provided is a brain stimulation simulation method according to a preset guide system using an external server, the method performed by a computing device, the method including: a first server generating a global matrix for performing brain stimulation simulation on a plurality of objects by using a plurality of brain models for each of the plurality of objects; and a second server being provided with the generated global matrix from the first server and performing the brain stimulation simulation on the plurality of objects by using the provided global matrix.

The need for an accurate, early, and precise estimation of expected delivery date (EDD) for the pregnant subject is vital. A system and method for predicting a day/date of delivery for a pregnant subject using one or more microbiome samples collected from the pregnant subject is provided. The disclosure relates to applying machine learning techniques on the microbiome characterization data corresponding to the biological sample(s) collected from the pregnant subject. The method further comprises using the predicted EDD to suitably plan and take required medical treatment or precautions or medical advice for the pregnant subject to prevent any pregnancy and/or delivery related complications and to manage the delivery appropriately. The disclosure also provides compositions of the microbiome data which can potentially influence the delivery date, or the method provides exemplary compositions of the microbiome data which plays vital role in estimating the EDD of the pregnant subject.

A system for smart pooling includes a computing device configured to obtain a feature datum, identify a predictive prevalence value as a function of the feature datum, wherein identifying the predictive prevalence value further comprises receiving a predictive training set correlating the feature datum with a probabilistic outcome, training a predictive machine-learning model as a function of the predictive training set, and identifying the predictive prevalence value as a function of the trained predictive machine-learning model and the feature datum, and determine an enhanced well count.