CPC A61B 5/02055 (2013.01) [A61B 5/0022 (2013.01); A61B 5/0245 (2013.01); A61B 5/02405 (2013.01); A61B 5/02416 (2013.01); A61B 5/349 (2021.01); A61B 5/361 (2021.01); A61B 5/681 (2013.01); A61B 5/7264 (2013.01); A61B 5/7267 (2013.01); A61B 5/7275 (2013.01); A61B 5/742 (2013.01); A61B 5/746 (2013.01); G16H 40/63 (2018.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); G16Z 99/00 (2019.02); A61B 5/021 (2013.01); A61B 5/02438 (2013.01); A61B 5/1118 (2013.01); A61B 5/6898 (2013.01); G16H 10/60 (2018.01); G16H 15/00 (2018.01); G16H 50/30 (2018.01)] | 20 Claims |
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 low-fidelity 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 configured to predict whether an event is normal or abnormal, wherein if the high-fidelity machine learning model predicts that the event will be abnormal, the high-fidelity machine learning model further calculates an amount of time the event will be abnormal; and in response to the event being abnormal, send a notification that a health of the user is abnormal.
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