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Artificial Intelligence and digital health have the potential to transformglobal health. However, having access to representative data to test andvalidate algorithms in realistic production environments is essential. Weintroduce HealthSyn, an open-source synthetic data generator of user behaviorfor testing reinforcement learning algorithms in the context of mobile healthinterventions. The generator utilizes Markov processes to generate diverse useractions, with individual user behavioral patterns that can change in reactionto personalized interventions (i.e., reminders, recommendations, andincentives). These actions are translated into actual logs using an ML-purposeddata schema specific to the mobile health application functionality includedwith HealthKit, and open-source SDK. The logs can be fed to pipelines to obtainuser metrics. The generated data, which is based on real-world behaviors andsimulation techniques, can be used to develop, test, and evaluate, both MLalgorithms in research and end-to-end operational RL-based interventiondelivery frameworks.

Synthetic Data Generator for Adaptive Interventions in Global Health