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AI Biotech/Diagnostics: Cardio
Search All Applications in AI Biotech/Diagnostics: Cardio
Application US20200107733
Published 2020-04-09
Machine Learning Health Analysis With A Mobile Device
Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's low-fidelity health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.
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- 1. An apparatus, comprising:
a processing device; a heath-indicator data sensor operatively coupled to the processing device; and a memory having instructions stored thereon that, when executed by the processing device, cause the processing device to:
receive measured low-fidelity health-indicator data and other-factor data of a user at a time, wherein the measured health-indicator data is obtained by the health-indicator data sensor;
input a set of data comprising the low-fidelity health-indicator data and the other-factor data into a trained high-fidelity machine learning model, wherein the trained high-fidelity machine learning model is to generate a prediction whether a high-fidelity health-indicator output of the user is normal or abnormal; and
in response to the prediction being abnormal, send a notification that a health of the user is abnormal.
- 12. A method, comprising:
receiving, by a processing device, measured low-fidelity health-indicator data and other-factor data of a user at a time, wherein the measured low-fidelity health-indicator data is obtained by a user health-indicator data sensor; inputting, by the processing device, data comprising the low-fidelity health-indicator data and other-factor data at the time into a trained high-fidelity machine learning model, wherein the trained high-fidelity machine learning model generates a prediction whether a high-fidelity health-indicator output of the user is normal or abnormal; and in response to the prediction being abnormal, sending a notification that a health of the user is abnormal.
