Synthetic Data Generator for Adaptive Interventions in Global Health
This addresses the problem of data scarcity for researchers and developers in global health, enabling more robust algorithm testing, though it is incremental as it builds on existing simulation and data generation methods.
The authors tackled the lack of representative data for testing reinforcement learning algorithms in mobile health by introducing HealthSyn, an open-source synthetic data generator that produces realistic user behavior logs based on Markov processes and real-world simulations.
Artificial Intelligence and digital health have the potential to transform global health. However, having access to representative data to test and validate algorithms in realistic production environments is essential. We introduce HealthSyn, an open-source synthetic data generator of user behavior for testing reinforcement learning algorithms in the context of mobile health interventions. The generator utilizes Markov processes to generate diverse user actions, with individual user behavioral patterns that can change in reaction to personalized interventions (i.e., reminders, recommendations, and incentives). These actions are translated into actual logs using an ML-purposed data schema specific to the mobile health application functionality included with HealthKit, and open-source SDK. The logs can be fed to pipelines to obtain user metrics. The generated data, which is based on real-world behaviors and simulation techniques, can be used to develop, test, and evaluate, both ML algorithms in research and end-to-end operational RL-based intervention delivery frameworks.