Sensor-based machine learning in a health prediction environment

A method for training a machine learning prediction system, comprising:

• (a) accessing, by the machine learning prediction system, a set of training data for a plurality of users of a population, the training data representative of physical statistics and symptoms for the plurality of users for each of a plurality of time periods, the training data collected by a wearable sensor;
• (b) training, by the machine learning prediction system, a machine learning model using the accessed set of training data, the machine learning model configured to predict, for a first acute health condition, acute health condition onset for a user based on physical statistics of the user, the physical statistics comprising data corresponding to (i) a weather condition corresponding to the user, (ii) a planned event corresponding to the user, and a (iii) a geographical location corresponding to the user;
• and
• (c) in response to determining, via the machine learning model, a probability of acute health condition onset for a user exceeds a threshold, performing one or more intervention actions on behalf of the target user.

A method comprising:

• (a) obtaining wearable sensor data comprising a plurality of time series measurements collected from a target user during a first time period;
• and
• (b) predicting, via a machine learning model, a recovery trajectory from an acute condition or debilitating event at least in part by processing the wearable sensor data comprising the plurality of time series measurements from the first time period, wherein the classifier is trained at least in part by:
• using a set of training data from a first population cohort comprising a plurality of users, the set of training data comprising a confirmed case of the acute condition or debilitating event of a first user of the plurality of users and wearable sensor data from the plurality of users, wearable sensor data from the first user of the plurality of users comprising physical statistics and symptoms collected over a plurality of time periods and associated with the acute condition or debilitating event, wherein at least one of the consecutive time periods is prior to an onset of the acute condition or debilitating event and at least one of the consecutive time periods is after the onset of the acute condition or debilitating event, wherein the first user is placed in the first population cohort based at least in part on a pattern of the wearable sensor data from the first user.

A system comprising:

• one or more processors;
• and
• one or more memories storing computer-executable instructions that, when executed, cause the one or more processors to perform operations comprising:
• (a) obtaining wearable sensor data comprising a plurality of time series measurements collected from a target user during a first time period;
• and
• (b) predicting, via a machine learning model, a recovery trajectory from an acute condition or debilitating event at least in part by processing the wearable sensor data comprising the plurality of time series measurements from the first time period, wherein the classifier is trained at least in part by:
• using a set of training data from a first population cohort comprising a plurality of users, the set of training data comprising a confirmed case of the acute condition or debilitating event of a first user of the plurality of users and wearable sensor data from the plurality of users, wearable sensor data from the first user of the plurality of users comprising physical statistics and symptoms collected over a plurality of time periods and associated with the acute condition or debilitating event, wherein at least one of the consecutive time periods is prior to an onset of the acute condition or debilitating event and at least one of the consecutive time periods is after the onset of the acute condition or debilitating event, wherein the first user is placed in the first population cohort based at least in part on a pattern of the wearable sensor data from the first user.