Illness Detection Based on Temperature Data

1 . A method for automatically detecting illness, comprising:

receiving temperature data associated with a user from a wearable device, the temperature data collected via the wearable device throughout a first time interval;

identifying baseline temperature data associated with the user based at least in part on the temperature data collected throughout the first time interval;

receiving additional temperature data associated with the user from the wearable device, the additional temperature data collected via the wearable device throughout a second time interval subsequent to the first time interval;

inputting the baseline temperature data and the additional temperature data into a classifier;

identifying, using the classifier, a satisfaction of one or more deviation criteria between the baseline temperature data and the additional temperature data; and

causing a graphical user interface of a user device to display an illness risk metric associated with the user based at least in part on the satisfaction of the one or more deviation criteria, the illness risk metric associated with a relative probability that the user will transition from a healthy state to an unhealthy state.

2 . The method of claim 1 , further comprising:

identifying baseline frequency content of the baseline temperature data associated with the user; and

identifying additional frequency content of the additional temperature data; and

inputting the baseline frequency content and the additional frequency content into the classifier, wherein identifying the satisfaction of the one or more deviation criteria is based at least in part on the baseline frequency content and the additional frequency content.

3 . The method of claim 1 , further comprising:

identifying a first high daytime temperature range within the baseline temperature data for at least a first day within the first time interval; and

identifying a second high daytime temperature range within the additional temperature data for at least a second day within the second time interval, wherein the first and second high daytime temperature ranges are greater than or equal to a percentile threshold of temperature readings collected from the user within the first and second days, respectively, wherein identifying the satisfaction of the one or more deviation criteria is based at least in part on the first high daytime temperature range, the second high daytime temperature range, or both.

4 . The method of claim 3 , wherein identifying satisfaction of the one or more deviation criteria comprises:

identifying a change between the first high daytime temperature range and the second high daytime temperature range exceeds a temperature change threshold.

5 . The method of claim 1 , further comprising:

identifying a first low daytime temperature range within the baseline temperature data for at least a first day within the first time interval; and

identifying a second low daytime temperature range within the additional temperature data for at least a second day within the second time interval, wherein the first and second low daytime temperature ranges are less than or equal to a percentile threshold of temperature readings collected from the user within the first and second days, respectively, wherein identifying the satisfaction of the one or more deviation criteria is based at least in part on the first low daytime temperature range, the second low daytime temperature range, or both.

6 . The method of claim 5 , wherein identifying satisfaction of the one or more deviation criteria comprises:

identifying a change between the first low daytime temperature range and the second low daytime temperature range exceeds a temperature change threshold.

7 . The method of claim 1 , further comprising:

identifying a first subset of the baseline temperature data which was collected by the wearable device within a daytime interval for each day within the first time interval; and

identifying a second subset of the additional temperature data which was collected by the wearable device within the daytime interval for each day within the second time interval, wherein inputting the temperature data into the classifier comprises inputting the first subset of the baseline temperature data and the second subset of the additional temperature data into the classifier.

8 . The method of claim 7 , further comprising:

identifying the daytime interval based at least in part on location information associated with the user, a sunrise-sunset calendar, an identified bed time associated with the user, an identified wake-up time associated with the user, or any combination thereof.

9 . The method of claim 1 , further comprising:

identifying location information associated with the user for at least a portion of the first time interval and at least a portion of the second time interval; and

inputting the location information into the classifier, wherein the classifier is configured to identify the satisfaction of the one or more deviation criteria based at least in part on the location information.

10 . The method of claim 9 , further comprising:

identifying ambient temperature data associated with a geographical position of the user based at least in part on the location information; and

inputting the ambient temperature data into the classifier, wherein identifying satisfaction of the one or more deviation criteria is based at least in part on the ambient temperature data.

11 . The method of claim 10 , further comprising:

identifying climate data, a time of year, or both, wherein identifying the ambient temperature data is based at least in part on the climate data, the time of year, or both.

12 . The method of claim 9 , wherein identifying the location information comprises:

receiving an indication of the location information from the user device.

13 . The method of claim 9 , wherein the location information comprises a geographical position of the user, a latitude of the user, or both.

14 . The method of claim 9 , further comprising:

identifying, using the classifier, one or more predictive weights associated with the additional temperature data based at least in part on the location information, the one or more predictive weights associated with a relative predictive accuracy for detecting illness, wherein identifying satisfaction of the one or more deviation criteria is based at least in part on the one or more predictive weights.

15 . The method of claim 14 , further comprising:

weighting, using the classifier, the additional temperature data based at least in part on the one or more predictive weights to generate weighted temperature data;

receiving additional physiological data associated with the user from the wearable device, the additional physiological data collected via the wearable device throughout the first time interval and the second time interval; and

inputting the additional physiological data into the classifier, wherein identifying the satisfaction of the one or more deviation criteria is based at least in part on the weighted temperature data, the additional physiological data, or a combination thereof.

16 . The method of claim 1 , wherein receiving the temperature data throughout the first time interval comprises:

receiving a plurality of temperature readings associated with the user in accordance with a temperature collection periodicity throughout each day of a plurality of days of the first time interval.

17 . The method of claim 1 , wherein the wearable device comprises a wearable ring device.

18 . The method of claim 1 , wherein the wearable device collects the physiological data from the user based on arterial blood flow.

19 . The method of claim 1 , wherein the user device comprises a user device associated with the user, a user device associated with an administrator associated with a group of users including the user, or both.

20 . The method of claim 1 , wherein the temperature data and the additional temperature data is associated with a plurality of users including the user, the temperature data and the additional temperature data collected via a plurality of wearable devices associated with the plurality of users, the method further comprising:

identifying baseline temperature data associated with each user of the plurality of users based at least in part on the received temperature data;

inputting the baseline temperature data for each user of the plurality of users into the classifier;

identifying, using the classifier, an illness risk metric associated with each user of the plurality of users based at least in part on the baseline temperature data for 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 plurality of users.

21 . An apparatus for automatically detecting illness, 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 temperature data associated with a user from a wearable device, the temperature data collected via the wearable device throughout a first time interval;

identify baseline temperature data associated with the user based at least in part on the temperature data collected throughout the first time interval;

receive additional temperature data associated with the user from the wearable device, the additional temperature data collected via the wearable device throughout a second time interval subsequent to the first time interval;

input the baseline temperature data and the additional temperature data into a classifier;

identify, using the classifier, a satisfaction of one or more deviation criteria between the baseline temperature data and the additional temperature data; and

cause a graphical user interface of a user device to display an illness risk metric associated with the user based at least in part on the satisfaction of the one or more deviation criteria, the illness risk metric associated with a relative probability that the user will transition from a healthy state to an unhealthy state.

22 . The apparatus of claim 21 , wherein the instructions are further executable by the processor to cause the apparatus to:

identify baseline frequency content of the baseline temperature data associated with the user; and

identify additional frequency content of the additional temperature data; and

input the baseline frequency content and the additional frequency content into the classifier, wherein identifying the satisfaction of the one or more deviation criteria is based at least in part on the baseline frequency content and the additional frequency content.

23 . The apparatus of claim 21 , wherein the instructions are further executable by the processor to cause the apparatus to:

identify a first high daytime temperature range within the baseline temperature data for at least a first day within the first time interval; and

identify a second high daytime temperature range within the additional temperature data for at least a second day within the second time interval, wherein the first and second high daytime temperature ranges are greater than or equal to a percentile threshold of temperature readings collected from the user within the first and second days, respectively, wherein identifying the satisfaction of the one or more deviation criteria is based at least in part on the first high daytime temperature range, the second high daytime temperature range, or both.

24 . The apparatus of claim 23 , wherein the instructions to identify satisfaction of the one or more deviation criteria are executable by the processor to cause the apparatus to:

identify a change between the first high daytime temperature range and the second high daytime temperature range exceeds a temperature change threshold.

25 . The apparatus of claim 21 , wherein the instructions are further executable by the processor to cause the apparatus to:

identify a first low daytime temperature range within the baseline temperature data for at least a first day within the first time interval; and

identify a second low daytime temperature range within the additional temperature data for at least a second day within the second time interval, wherein the first and second low daytime temperature ranges are less than or equal to a percentile threshold of temperature readings collected from the user within the first and second days, respectively, wherein identifying the satisfaction of the one or more deviation criteria is based at least in part on the first low daytime temperature range, the second low daytime temperature range, or both.