Abstract: In an aspect, a computer-implemented method for assembling a pool of high-risk subjects for developing an acute illness is disclosed. The method comprises obtaining, from a plurality of subjects, (i) one or more responses to one or more health queries, and (ii) geographic incidence data for the acute illness. The method next comprises predicting, using a machine learning model, a risk of developing the acute illness for the plurality of subjects based on the one or more responses and the geographic incidence data. The method next comprises identifying the pool of high-risk subjects from the plurality of subjects, wherein the risk of developing the acute illness for each subject of the pool of high-risk subjects satisfies a threshold. Finally, the method comprises outputting the pool of high-risk subjects.

Abstract: A machine learning prediction system can analyze a dataset of users with self-reported symptoms and associated data from a wearable device to impact measure the impact of an acute health condition (such as the flu) at the population level. The machine learning prediction system can train a machine learning model to recognize individual acute health condition patterns based on differences in user activity with respect to the characteristics of determined baseline periods. For example, per-individual normalized change with respect to baseline aggregated at the population level can be used to determine individual acute health condition patterns and predict the onset of certain acute health conditions using a trained machine learning model. In response to predictions, the machine learning prediction system can take interventions to manage the impact of a predicted acute health condition on an individual.

Abstract: A machine learning prediction system can analyze a dataset of users with self-reported symptoms and associated data from a wearable device to impact measure the impact of an acute health condition (such as the flu) at the population level. The machine learning prediction system can train a machine learning model to recognize individual acute health condition patterns based on differences in user activity with respect to the characteristics of determined baseline periods. For example, per-individual normalized change with respect to baseline aggregated at the population level can be used to determine individual acute health condition patterns and predict the onset of certain acute health conditions using a trained machine learning model. In response to predictions, the machine learning prediction system can take interventions to manage the impact of a predicted acute health condition on an individual.