Universität zu Köln

Kuo, Nicholas I-Hsien, Polizzotto, Mark N., Finfer, Simon ORCID: 0000-0002-2785-5864, Garcia, Federico, Sonnerborg, Anders, Zazzi, Maurizio, Boehm, Michael, Kaiser, Rolf, Jorm, Louisa and Barbieri, Sebastiano (2022). The Health Gym: synthetic health-related datasets for the development of reinforcement learning algorithms. Sci. Data, 9 (1). BERLIN: NATURE PORTFOLIO. ISSN 2052-4463

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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 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. The datasets were created using a novel generative adversarial network (GAN). The distributions of variables, and correlations between variables and trends in variables 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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Kuo, Nicholas I-Hsien UNSPECIFIED UNSPECIFIED UNSPECIFIED Polizzotto, Mark N. UNSPECIFIED UNSPECIFIED UNSPECIFIED Finfer, Simon UNSPECIFIED orcid.org/0000-0002-2785-5864 UNSPECIFIED Garcia, Federico UNSPECIFIED UNSPECIFIED UNSPECIFIED Sonnerborg, Anders UNSPECIFIED UNSPECIFIED UNSPECIFIED Zazzi, Maurizio UNSPECIFIED UNSPECIFIED UNSPECIFIED Boehm, Michael UNSPECIFIED UNSPECIFIED UNSPECIFIED Kaiser, Rolf UNSPECIFIED UNSPECIFIED UNSPECIFIED Jorm, Louisa UNSPECIFIED UNSPECIFIED UNSPECIFIED Barbieri, Sebastiano UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-690386
DOI:	10.1038/s41597-022-01784-7
Journal or Publication Title:	Sci. Data
Volume:	9
Number:	1
Date:	2022
Publisher:	NATURE PORTFOLIO
Place of Publication:	BERLIN
ISSN:	2052-4463
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language ANTIRETROVIRAL THERAPY; INFORMATION; NETWORKS Multiple languages Multidisciplinary Sciences Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/69038