The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithms

TL;DR

The paper introduces the Health Gym, a collection of realistic synthetic medical datasets generated by GANs, enabling safe and accessible development of reinforcement learning algorithms in healthcare.

Contribution

It presents a novel approach to creating synthetic health data that closely mimics real data, facilitating research without compromising patient privacy.

Findings

Synthetic datasets mirror real data distributions and correlations.

Low risk of sensitive information disclosure.

Supports development of reinforcement learning in healthcare.

Abstract

In recent years, the machine learning research community has benefited tremendously from the availability of openly accessible benchmark datasets. Clinical data are usually not openly available due to their highly confidential nature. This has hampered the development of reproducible and generalisable machine learning applications in health care. Here we introduce the Health Gym - a growing collection of highly realistic synthetic medical datasets that can be freely accessed to prototype, evaluate, and compare machine learning algorithms, with a specific focus on reinforcement learning. The three synthetic datasets described in this paper present patient cohorts with acute hypotension and sepsis in the intensive care unit, and people with human immunodeficiency virus (HIV) receiving antiretroviral therapy in ambulatory care. The datasets were created using a novel generative adversarial network (GAN). The distributions of variables, and correlations between variables and trends over time in the synthetic datasets mirror those in the real datasets. Furthermore, the risk of sensitive information disclosure associated with the public distribution of the synthetic datasets is estimated to be very low.