Machine learning health analysis with a mobile device

Patent metadata

Field	Value
Publication number	US-11877830-B2
Application number	US-201916580574-A
Country	US
Kind code	B2
Filing date	Sep 24, 2019
Priority date	Dec 12, 2013
Publication date	Jan 23, 2024
Grant date	Jan 23, 2024

Abstract

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.

First claim

What is claimed is:

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.

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.

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.

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.

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.

6. The apparatus according to claim 5 , wherein the prediction is sampled according to a sampling technique selected from the group consisting of: the prediction at a maximum probability; and random sampling the prediction from the probability distribution.

7. The apparatus according to claim 5 , wherein an averaged prediction is determined by averaging, using an averaging method, the prediction over a period of time steps, and wherein the averaged prediction is used to determine if the event is normal or abnormal.

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.

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.

10. The apparatus according to claim 1 , wherein the low-fidelity health-indicator data comprises a record of a heart rate of the user 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 the heart rate of the user prior to the time.

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

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.

15. The method according to claim 12 , wherein the low-fidelity health-indicator data comprises a record of a heart rate of the user 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 the heart rate of the user prior to the time, and the notification comprises and an indication to take an ECG.

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.

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.

18. The method according to claim 17 , wherein the prediction is sampled according to a sampling technique selected from the group consisting of: the prediction at a maximum probability; and random sampling the prediction from the probability distribution.

19. The method according to claim 17 , wherein an averaged prediction is determined by averaging, using an averaging method, the prediction over a period of time steps, and wherein the averaged prediction is used to determine if the event is normal or abnormal.

20. The method according to claim 12 , further comprising:
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-fide

Assignees

• Alivecor Inc

Inventors

• Valys Alexander Vainius
• Petterson Frank Losasso
• Galloway Conner Daniel Cross
• Albert David E
• Gopalakrishnan Ravi
• Korzinov Lev
• Wang Fei
• Thomson Euan
• Srivastava Nupur
• Dawood Omar
• Abuzeid Iman

Classifications

• A61B5/349

  Detecting specific parameters of the electrocardiograph cycle · CPC title

• A61B5/02055 Primary

  Simultaneously evaluating both cardiovascular condition and temperature · CPC title

• A61B5/0022

  Monitoring a patient using a global network, e.g. telephone networks, internet · CPC title

• A61B5/0245

  by using sensing means generating electric signals, {i.e. ECG signals} · CPC title

• A61B5/02405

  Determining heart rate variability · CPC title