Health Monitoring Platform for Illness Detection

1 . A method for automatic illness detection, comprising:

receiving physiological data associated with one or more users, the physiological data being continuously collected via one or more wearable devices associated with the respective one or more users;

identifying baseline physiological data for each user of the one or more users based at least in part on a first subset of the physiological data being continuously collected from each respective user via a respective wearable device of the one or more wearable devices;

inputting a second subset of the physiological data and the baseline physiological data for each user of the one or more users into a classifier;

identifying, using the classifier, an illness risk metric associated with each user of the one or more users based at least in part on the second subset of the physiological data and the baseline physiological data associated with each respective user; and

causing a graphical user interface of an administrator user device to display at least one illness risk metric associated with at least one user of the one or more users, the at least one illness risk metric associated with a relative probability that the at least one user will transition from a healthy state to an unhealthy state.

2 . The method of claim 1 , further comprising:

causing the graphical user interface of the administrator user device to display one or more illness risk metrics associated with the one or more users, wherein an order of the one or more illness risk metrics is based at least in part on a comparison of the one or more illness risk metrics.

3 . The method of claim 1 , further comprising:

causing a graphical user interface of a user device associated with the at least one user to display one or more notifications based at least in part on the at least one illness risk metric.

4 . The method of claim 3 , wherein the one or more notifications comprise a recommendation to schedule a doctor appointment, a recommendation to stay home, a recommendation to prepare for a potential illness by resting or hydrating, or any combination thereof.

5 . The method of claim 1 , wherein identifying the illness risk metric associated with each user of the one or more users comprises:

identifying a first illness risk metric associated with a first user of the one or more users based at least in part on a first subset of the physiological data associated with the first user; and

selectively modifying a second illness risk metric associated with a second user based at least in part on the first illness risk metric, and based at least in part on a potential contact between the first user and the second user.

6 . The method of claim 1 , further comprising:

causing a graphical user interface of a user device associated with the at least one user to display a notification which indicates one or more contributing factors for the at least one illness risk metric.

7 . The method of claim 1 , further comprising:

receiving one or more user inputs associated with illness risk metrics via the administrator user device, wherein identifying the illness risk metrics, causing the administrator user device to display the at least one illness risk metric, or both, is based at least in part on the one or more user inputs.

8 . The method of claim 7 , wherein the one or more user inputs comprise a first threshold for identifying the illness risk metrics, a second threshold for reporting the illness risk metrics to the administrator user device, or both.

9 . The method of claim 1 , wherein the administrator user device is associated with an administrator of an organization associated with the one or more users, a health care professional associated with the one or more users, an employer associated with the one or more users, a manager associated with the one or more users, a coach associated with the one or more users, or any combination thereof.

10 . The method of claim 1 , wherein the physiological data comprises heart rate variability data for the at least one user, and wherein the baseline physiological data for the at least one user comprises baseline heart rate variability data determined based at least in part on a first subset of the heart rate variability data collected throughout a reference time interval for the at least one user, the method further comprising:

inputting the baseline heart rate variability data and a second subset of the heart rate variability data collected throughout a second time interval subsequent to the reference time interval into the classifier; and

identifying, using the classifier, a satisfaction of one or more deviation criteria between the second subset of the heart rate variability data and the baseline heart rate variability data, wherein identifying the illness risk metric for the at least one user is based at least in part on the satisfaction of the one or more deviation criteria.

11 . The method of claim 1 , wherein the physiological data comprises temperature data for the at least one user, and wherein the baseline physiological data for the at least one user comprises baseline temperature data determined based at least in part on a first subset of the temperature data collected throughout a reference time interval for the at least one user, the method further comprising:

inputting the baseline temperature data and a second subset of the temperature data collected throughout a second time interval subsequent to the reference time interval into the classifier; and

identifying, using the classifier, a satisfaction of one or more deviation criteria between the second subset of the temperature data and the baseline temperature data, wherein identifying the illness risk metric associated with the at least one user is based at least in part on the satisfaction of the one or more deviation criteria.

12 . The method of claim 1 , the method further comprising:

identifying physical activity data, sleep data, or both, associated with the at least one user based at least in part on the physiological data;

identifying baseline physical activity data for the at least one user, baseline sleep data for the at least one user, or both, based at least part on a first subset of the physical activity data collected throughout a reference time interval and a first subset of the sleep data collected throughout the reference time interval, respectively;

inputting the baseline physical activity data, the baseline sleep data, or both, into the classifier;

inputting a second subset of the physical activity data collected throughout a second time interval subsequent to the reference time interval, a second subset of the sleep data collected throughout the second time interval, or both, into the classifier;

identifying, using the classifier, a satisfaction of one or more deviation criteria between the baseline physical activity data and the second subset of the physical activity data, between the baseline sleep data and the second subset of the sleep data, or both; and

identifying, using the classifier, the illness risk metric for the at least one user based at least in part on the satisfaction of the one or more deviation criteria.

13 . The method of claim 1 , the method further comprising:

identifying a menstrual cycle model associated with a menstrual cycle for the at least one user;

inputting the menstrual cycle model into the classifier; and

identifying, using the classifier, the at least one illness risk metric associated with the at least one user based at least in part on the menstrual cycle model and the second subset of the physiological data.

14 . The method of claim 1 , wherein at least one wearable device of the one or more wearable devices comprises a wearable ring device.

15 . The method of claim 1 , wherein at least one wearable device of the one or more wearable devices collects the physiological data from a respective user based on arterial blood flow.

16 . An apparatus, comprising:

a processor;

memory coupled with the processor; and

instructions stored in the memory and executable by the processor to cause the apparatus to:

receive physiological data associated with one or more users, the physiological data being continuously collected via one or more wearable devices associated with the respective one or more users;

identify baseline physiological data for each user of the one or more users based at least in part on a first subset of the physiological data being continuously collected from each respective user via a respective wearable device of the one or more wearable devices;

input a second subset of the physiological data and the baseline physiological data for each user of the one or more users into a classifier;

identify, using the classifier, an illness risk metric associated with each user of the one or more users based at least in part on the second subset of the physiological data and the baseline physiological data associated with each respective user; and

cause a graphical user interface of an administrator user device to display at least one illness risk metric associated with at least one user of the one or more users, the at least one illness risk metric associated with a relative probability that the at least one user will transition from a healthy state to an unhealthy state.

17 . The apparatus of claim 16 , wherein the instructions are further executable by the processor to cause the apparatus to:

cause the graphical user interface of the administrator user device to display one or more illness risk metrics associated with the one or more users, wherein an order of the one or more illness risk metrics is based at least in part on a comparison of the one or more illness risk metrics.

18 . The apparatus of claim 16 , wherein the instructions are further executable by the processor to cause the apparatus to:

cause a graphical user interface of a user device associated with the at least one user to display one or more notifications based at least in part on the at least one illness risk metric.

19 . The apparatus of claim 18 , wherein the one or more notifications comprise a recommendation to schedule a doctor appointment, a recommendation to stay home, a recommendation to prepare for a potential illness by resting or hydrating, or any combination thereof.

20 . The apparatus of claim 16 , wherein the instructions to identify the illness risk metric associated with each user of the one or more users are executable by the processor to cause the apparatus to:

identify a first illness risk metric associated with a first user of the one or more users based at least in part on a first subset of the physiological data associated with the first user; and

selectively modify a second illness risk metric associated with a second user based at least in part on the first illness risk metric, and based at least in part on a potential contact between the first user and the second user.