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AI Biotech/Diagnostics: Cardio
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Application US20190038148
Published 2019-02-07
Health 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 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 at a first time, wherein measured health-indicator data is obtained by the health-indicator data sensor;
input a set of data comprising the health-indicator data and other-factor data at the first time into a trained high-fidelity machine learning model, wherein the trained high-fidelity machine learning model generate a prediction whether a high-fidelity health-indicator signal of the user is normal or abnormal; and
in response to the prediction being abnormal, send a notification that the user's health is abnormal.
- 13. A method, comprising:
receiving, by a processing device, measured low fidelity health-indicator data and other-factor data at a first time, wherein measured health-indicator data is obtained by a user health-indicator data sensor; inputting, by the processing device, data comprising the health-indicator data and other-factor data at the first 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 signal of the user is normal or abnormal; and in response to the prediction being abnormal, sending a notification that the user's health is abnormal.
- 17. A method comprising:
receiving a set of training data, wherein the training data comprises a set of low-fidelity health-indicator data, a corresponding set of other-factor data, wherein the low-fidelity health-indicator data is labeled with a corresponding set of actual high-fidelity labels; inputting, by a processing device, an interval of low-fidelity health-indicator data and corresponding other-factor data into an untrained machine learning model to generate a predicted high-fidelity label; updating, by the processing device, the machine learning model based on comparing the predicted high-fidelity label to an actual high-fidelity label corresponding to the interval of low-fidelity health-indicator data.
