Synthetic Data Generator for Adaptive Interventions in Global Health

Aditya Rastogi, Juan Francisco Garamendi, Ana Fernandez del Rio, Anna Guitart Atienza, Moiz Hassan Khan, Dexian Tang, Africa Perianez Santiago

Keywords: simulation, reinforcement learning, health care interventions, markov chain, behaviour prediction, behavioural logs, synthetic data generator, multi armed bandits, behaviour simulation, SDK, mobile health

TL;DR: An open-source synthetic data generator of user behavior for testing reinforcement learning algorithms in the context of mobile health interventions using open-source SDK for tracking, organizing, and labeling data ready for machine learning models.

Abstract: 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.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2303.01954/code)