Application US20200107733

MACHINE LEARNING HEALTH ANALYSIS WITH A MOBILE DEVICE
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US20200107733

1. An apparatus, comprising: (8) (3)

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.

2. The apparatus according to claim 1, wherein the trained high-fidelity machine learning model comprises one or more of a trained high-fidelity generative neural network, a trained recurrent neural network (RNN), a trained feed-forward neural network, or a trained feed-forward neural network. (0)

3. The apparatus according to claim 1, wherein the trained high-fidelity machine learning model is trained on measured user health-indicator data labeled with user-specific high-fidelity measurement data. (0)

4. The apparatus according to claim 1, wherein the trained high-fidelity machine learning model is trained on low-fidelity health-indicator data labeled with high-fidelity measurement data, wherein the low-fidelity health-indicator data and the high-fidelity measurement data is from a population of subjects. (0)

5. The apparatus according to claim 1, wherein the high-fidelity machine learning model outputs a probability distribution, wherein the prediction is sampled from the probability distribution. (2) (0)

8. The apparatus according to claim 1, wherein the apparatus is selected from the group consisting of: a smart watch; a fitness band; a computer tablet; and a laptop computer. (0)

9. The apparatus according to claim 1, wherein each of the low-fidelity health-indicator data and other factor-data are time segments of data over a time period. (0)

10. The apparatus according to claim 1, wherein the low-fidelity health-indicator data comprises a record of heart rate prior to the time, the other-factor data comprises a record of activity level, and the prediction comprises a prediction that the user experienced atrial fibrillation during the record of heart rate prior to the time. (0)

11. The apparatus according to claim 1, wherein the processing device is further to: (0) (3)

receive a set of training data, wherein the training data comprises labeled low-fidelity health-indicator data from a population of individuals, corresponding other-factor data from the population individuals, wherein the labeled low-fidelity indicator data is labeled with corresponding high-fidelity data from the population of individuals;

input an interval of the labeled low-fidelity health-indicator data and the corresponding other-factor data into the trained high-fidelity machine learning model; and

update the labeled high-fidelity machine learning model by comparing an output from the trained high-fidelity machine learning model to the labeled high-fidelity data.

12. A method, comprising: (6) (3)

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.

13. The method according to claim 12, wherein the trained high-fidelity machine learning model comprises one or more of a trained high-fidelity generative neural network, a trained recurrent neural network (RNN), a trained feed-forward neural network, or a trained feed-forward neural network. (0)

14. The method according to claim 12, wherein each of the low-fidelity health-indicator data and the other factor-data are time segments of data over a time period. (0)

15. The method according to claim 12, wherein the low-fidelity health-indicator data comprises a record of heart rate prior to the time, the other-factor data comprises a record of activity level, the prediction comprises a prediction that the user experienced atrial fibrillation during the record of heart rate prior to the time, and the notification comprises and an indication to take an ECG. (0)

16. The method according to claim 12, wherein the trained high-fidelity machine learning model is trained on low-fidelity health-indicator data labeled with high-fidelity measurement data, wherein the low-fidelity health-indicator data and the high-fidelity measurement data is from a population of subjects. (0)

17. The method according to claim 12, wherein the high-fidelity machine learning model outputs a probability distribution, wherein the prediction is sampled from the probability distribution. (2) (0)

20. The method according to claim 12, further comprising: (0) (3)

receiving a set of training data, wherein the training data comprises labeled low-fidelity health-indicator data from a population of individuals, corresponding other-factor data from the population individuals, wherein the labeled low-fidelity indicator data is labeled with corresponding high-fidelity data from the population of individuals;

inputting an interval of the labeled low-fidelity health-indicator data and the corresponding other-factor data into the trained high-fidelity machine learning model; and

updating the labeled high-fidelity machine learning model by comparing an output from the trained high-fidelity machine learning model to the labeled high-fidelity data.

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Application US20200107733

MACHINE LEARNING HEALTH ANALYSIS WITH A MOBILE DEVICE

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