Synthetic Data Generator for Adaptive Interventions in Global Health
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 40 references that are in the map. First posted on arXiv 3 Mar 2023.
Builds on
14 of 40 references.
References · 14
- 2016OpenAI Gymcited by 1,684
- 2021How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Modelscited by 193
- 2021DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networkscited by 109
- 2019RecSim: A Configurable Simulation Platform for Recommender Systemscited by 91
- 2019Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activitycited by 70
- 2016A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patientscited by 38
- 2022Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelinescited by 22
- 2020MARS-Gym: A Gym framework to model, train, and evaluate Recommender Systems for Marketplacescited by 13
- 2020Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methodscited by 13
- 2018Measuring the quality of Synthetic data for use in competitionscited by 11
- 2021Midwifery Learning and Forecasting: Predicting Content Demand with User-Generated Logscited by 6
- 2021A Recommendation System to Enhance Midwives' Capacities in Low-Income Countriescited by 5
2 more
- 2019Policy Learning for Malaria Controlcited by 3
- 2021Data-Efficient Reinforcement Learning for Malaria Controlcited by 2
Built on by
Not cited yet.