A computer system includes memory hardware and processor hardware configured to execute stored instructions. The instructions include training a machine learning model with the historical feature vector inputs including multiple audio data entries and multiple claims data entries, to generate a condition likelihood output indicative of a specified condition associated with one of multiple historical database entities. The instructions include for each of a set of multiple database entities, generating a feature vector input according to audio data and the claims data associated with the entity, processing the feature vector input with the machine learning model to generate the condition likelihood output, and assigning the database entity to an identified condition subset in response to determining that the condition likelihood output is greater than a specified likelihood threshold. The instructions include transforming a user interface to display the condition likelihood output associated with the database entity.

A computer system includes memory hardware and processor hardware configured to execute stored instructions. The instructions include training a machine learning model with the historical feature vector inputs including multiple audio data entries and multiple claims data entries, to generate a condition likelihood output indicative of a specified condition associated with one of multiple historical database entities. The instructions include for each of a set of multiple database entities, generating a feature vector input according to audio data and the claims data associated with the entity, processing the feature vector input with the machine learning model to generate the condition likelihood output, and assigning the database entity to an identified condition subset in response to determining that the condition likelihood output is greater than a specified likelihood threshold. The instructions include transforming a user interface to display the condition likelihood output associated with the database entity.

Disclosed is a method of identifying dementia using at least one processor of a device according to some embodiments of the present disclosure. More particularly, the method may include performing a first task of causing for a user terminal to display a first screen including a sentence; performing a second task of causing for the user terminal to acquire an image including user’s eyes in association with displaying a moving object instead of the first screen; and performing a third task of causing for the user terminal to acquire a recording file in association with displaying a second screen in which the sentence is hidden, wherein the first task includes a sub-task of causing color of at least one word constituting the sentence included in the first screen to be sequentially changed.

Disclosed is a method of identifying dementia using at least one processor of a device according to some embodiments of the present disclosure. More particularly, the method may include performing a first task of causing for a user terminal to display a first screen including a sentence; performing a second task of causing for the user terminal to acquire an image including user’s eyes in association with displaying a moving object instead of the first screen; and performing a third task of causing for the user terminal to acquire a recording file in association with displaying a second screen in which the sentence is hidden, wherein the first task includes a sub-task of causing color of at least one word constituting the sentence included in the first screen to be sequentially changed.

A method for predicting the presence of a malady of the respiratory system in a subject comprising: operating at least one electronic processor to transform one or more sounds of the subject that are associated with the malady into corresponding one or more image representations of said sounds; applying said one or more representations to at least one pattern classifier trained to predict the presence of the malady; and operating said processor to predict the presence of the malady in the subject based on at least one output of the at least one pattern classifier.

A method for predicting the presence of a malady of the respiratory system in a subject comprising: operating at least one electronic processor to transform one or more sounds of the subject that are associated with the malady into corresponding one or more image representations of said sounds; applying said one or more representations to at least one pattern classifier trained to predict the presence of the malady; and operating said processor to predict the presence of the malady in the subject based on at least one output of the at least one pattern classifier.

A virtual agent instructs a responding person to perform specific verbal exercises. Audio and image inputs from the responding person's performance of the exercises are used to identify speech, video, cognitive, and/or respiratory biomarkers, which are then used to evaluate speech motor function and/or neurological health. Contemplated exercises include test aspects of oral motor proficiency, sustained phonation, diadochokinesis, reading speech, spontaneous speech, spirometry, picture description, and emotion elicitation. Metrics from evaluation of the responding person's performance are advantageously produced automatically, and are presented in spreadsheet format.

A virtual agent instructs a responding person to perform specific verbal exercises. Audio and image inputs from the responding person's performance of the exercises are used to identify speech, video, cognitive, and/or respiratory biomarkers, which are then used to evaluate speech motor function and/or neurological health. Contemplated exercises include test aspects of oral motor proficiency, sustained phonation, diadochokinesis, reading speech, spontaneous speech, spirometry, picture description, and emotion elicitation. Metrics from evaluation of the responding person's performance are advantageously produced automatically, and are presented in spreadsheet format.

A digital stethoscope includes a stethoscope housing defining a housing edge. The digital stethoscope also includes a surface region secured to the stethoscope housing at the housing edge, and a number of microphones. The digital stethoscope also includes a processing device disposed within the stethoscope housing and in communication with the microphones. The processing device receives the digital audio data from the microphones.

A digital stethoscope includes a stethoscope housing defining a housing edge. The digital stethoscope also includes a surface region secured to the stethoscope housing at the housing edge, and a number of microphones. The digital stethoscope also includes a processing device disposed within the stethoscope housing and in communication with the microphones. The processing device receives the digital audio data from the microphones.