To address this challenge, this paper introduces the Health Gym project–a collection of highly realistic synthetic medical datasets that can be freely accessed to facilitate the development of machine learning (ML) algorithms, with a specific focus on RL. Health-related data is, however, not as easily accessible due to privacy concerns around the disclosure of private information. The success of RL was greatly facilitated by the availability of standard benchmark problems: tasks with publicly available datasets which allowed the research community to develop, test, and compare RL algorithms ( e.g., OpenAI Gym 4, DeepMind Lab 5, and D4RL 6). Recent studies that combine RL with neural networks have achieved super-human performances in tasks from video games 2 to complex board games 3. Reinforcement learning 1 (RL) is an area of artificial intelligence (AI) which learns a behavioural policy–a mapping from states to actions–which maximises a cumulative reward in an evolving environment. Furthermore, the risk of sensitive information disclosure associated with the public distribution of the synthetic datasets is estimated to be very low. The distributions of variables, and correlations between variables and trends in variables over time in the synthetic datasets mirror those in the real datasets. The datasets were created using a novel generative adversarial network (GAN). 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. 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. This has hampered the development of reproducible and generalisable machine learning applications in health care. Clinical data are usually not openly available due to their confidential nature. In recent years, the machine learning research community has benefited tremendously from the availability of openly accessible benchmark datasets.
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