The digital personalized medicine system uses digital data to assess or diagnose symptoms of a subject to provide personalized or more appropriate therapeutic interventions and improved diagnoses. The use of prioritized questions and answers with associated feature importance can be used to assess mental function and allow a subject to be diagnosed with fewer questions, such that diagnosis can be repeated more often and allow the dosage to be adjusted more frequently. Pharmacokinetics of the subject can be estimated based on demographic data and biomarkers or measured, in order to determine a treatment plan for the subject. Also, biomarkers can be used to determine when the patient may be at risk for experiencing undesirable side effects and the treatment plan adjusted accordingly.

A home occupant detection and monitoring system has a sensor unit having a radio wave transmitter, a radio wave receiver, and a wireless transmitter configured to detect and receive vital signs of an occupant; a user interface having a microcontroller, a wireless receiver configured to receive the wireless signals transmitted from the sensor unit, a means for user input, and a network card; and a means for alerting occupants and third-parties to a triggering event; wherein the microcontroller, based upon logic, activates the alerting means at the triggering event.

A method includes: obtaining, by a computer, subject response data including audio data, video data, or audio-video data of a subject's response to one or more stimuli presented to the subject over a defined time period; extracting at least one subject descriptor from the subject response data as a function of time, the at least one subject descriptor including data characterizing involuntary or voluntary action of the subject in response to the one or more stimuli; deriving, from the at least one subject descriptor, a first biomarker characterizing a behavior of the subject in response to the one or more stimuli, in which a value of the first biomarker is indicative of a severity of a disease in the subject; and outputting a disease severity level for the disease as a function of the value of the first biomarker.

There is provided herein a method and system for assisting speech/language therapy practice utilizing a voice interactive artificial intelligence-powered virtual assistant system.

In some aspects, a device wearable by a person includes a sensor configured to detect a signal from the brain of the person and a transducer configured to apply to the brain an ultrasound signal. The ultrasound signal has a low power density and is substantially non-destructive with respect to tissue when applied to the brain.

In some aspects, a device includes a sensor configured to detect a signal from the brain of the person and a plurality of transducers, each configured to apply to the brain an acoustic signal. One of the plurality of transducers is selected using a statistical model trained on data from prior signals detected from the brain.

The present disclosure provides a system for predicting a disease state based on speech occurrences. A feature extraction module extracts a plurality of lingual features from a speech record of the speech occurrence. The lingual features are chosen based on a correlation between the lingual features and the disease state in at least a first language and a second language. The lingual features are consistent for transcripts in at least the first language and the second language. A prediction module including a trained classification model generates a prediction of the disease state for speech occurrences in at least the first language and the second language using the lingual features extracted from the speech records.

Disclosed herein is a method and a processor for determining health of an individual. The method comprising, in a processor, determining if change information indicative of a change in a plurality of characteristics associated with the individual satisfies a predetermined change condition.

A system and method for monitoring cardiovascular and brain functions in combination with a physiological detection device, which uses a smart wearable device to detect physiological data such as heart rate and pulse pressure of a user, and transmits the physiological data to an arithmetic function. An electronic device in which a preset function and a calculation formula are built in, and the physiological data can be converted into corresponding determination parameters to monitor the possibility and risk of cardiovascular diseases and neurodegenerative diseases.

Disclosed herein are platforms, systems, devices, methods and media for model optimization and data analysis using machine learning. Input data can be processed and analyzed to identify relevant discriminating features, which can be modeled using a plurality of machine learning models. A computing device can be configured with one or more optimized models for categorizing input data.