ArXiv Preprint
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.
Aditya Rastogi, Juan Francisco Garamendi, Ana Fernández del Río, Anna Guitart, Moiz Hassan Khan, Dexian Tang, África Periáñez
2023-03-03
