# Synthetic Data Generator for Adaptive Interventions in Global Health

arXiv: https://arxiv.org/abs/2303.01954 · HTML: https://mlsanity.com/paper/2303.01954/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 40 references that are in the map.
- First posted on arXiv 3 Mar 2023.

## Builds on (most cited first, 14 of 14)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models](https://mlsanity.com/paper/2102.08921/) (2021)
- [DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks](https://mlsanity.com/paper/2110.12884/) (2021)
- [RecSim: A Configurable Simulation Platform for Recommender Systems](https://mlsanity.com/paper/1909.04847/) (2019)
- [Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity](https://mlsanity.com/paper/1909.03539/) (2019)
- [A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patients](https://mlsanity.com/paper/1605.04070/) (2016)
- [Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines](https://mlsanity.com/paper/2206.03944/) (2022)
- [MARS-Gym: A Gym framework to model, train, and evaluate Recommender Systems for Marketplaces](https://mlsanity.com/paper/2010.07035/) (2020)
- [Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods](https://mlsanity.com/paper/2012.04580/) (2020)
- [Measuring the quality of Synthetic data for use in competitions](https://mlsanity.com/paper/1806.11345/) (2018)
- [Midwifery Learning and Forecasting: Predicting Content Demand with User-Generated Logs](https://mlsanity.com/paper/2107.02480/) (2021)
- [A Recommendation System to Enhance Midwives' Capacities in Low-Income Countries](https://mlsanity.com/paper/2111.01786/) (2021)
- [Policy Learning for Malaria Control](https://mlsanity.com/paper/1910.08926/) (2019)
- [Data-Efficient Reinforcement Learning for Malaria Control](https://mlsanity.com/paper/2105.01620/) (2021)

## 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.