Enriching Data Science and Health Care Education: Application and Impact of Synthetic Data Sets Through the Health Gym Project

Large-scale medical data sets are vital for hands-on education in health data science but are often inaccessible due to privacy concerns. Addressing this gap, we developed the Health Gym project, a free and open-source platform designed to generate synthetic health data sets applicable to various ar...

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Bibliographic Details
Published in:	JMIR medical education Vol. 10; p. e51388
Main Authors:	Kuo, Nicholas I-Hsien, Perez-Concha, Oscar, Hanly, Mark, Mnatzaganian, Emmanuel, Hao, Brandon, Di Sipio, Marcus, Yu, Guolin, Vanjara, Jash, Valerie, Ivy Cerelia, de Oliveira Costa, Juliana, Churches, Timothy, Lujic, Sanja, Hegarty, Jo, Jorm, Louisa, Barbieri, Sebastiano
Format:	Journal Article
Language:	English
Published:	Canada JMIR Publications 16-01-2024
Subjects:	Artificial Intelligence Confidentiality Data Science Datasets Drug resistance Ethics Exercise Generative artificial intelligence Health Education HIV HIV Infections - drug therapy Human immunodeficiency virus Humans Hypotension Multimedia Science education Scientists Sepsis Viewpoint generative adversarial networks hypotension human immunodeficiency virus (HIV) medical education accessibility educational purposes science education health care AI privacy antiretroviral therapy (ART) HIV data science data sets data privacy generative model sepsis
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Summary:	Large-scale medical data sets are vital for hands-on education in health data science but are often inaccessible due to privacy concerns. Addressing this gap, we developed the Health Gym project, a free and open-source platform designed to generate synthetic health data sets applicable to various areas of data science education, including machine learning, data visualization, and traditional statistical models. Initially, we generated 3 synthetic data sets for sepsis, acute hypotension, and antiretroviral therapy for HIV infection. This paper discusses the educational applications of Health Gym's synthetic data sets. We illustrate this through their use in postgraduate health data science courses delivered by the University of New South Wales, Australia, and a Datathon event, involving academics, students, clinicians, and local health district professionals. We also include adaptable worked examples using our synthetic data sets, designed to enrich hands-on tutorial and workshop experiences. Although we highlight the potential of these data sets in advancing data science education and health care artificial intelligence, we also emphasize the need for continued research into the inherent limitations of synthetic data.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2369-3762 2369-3762
DOI:	10.2196/51388