  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.
