Enriching Data Science and Health Care Education: Application and Impact of Synthetic Data Sets Through the Health Gym Project - Nicholas I-Hsien Kuo; Oscar Perez-Concha; Mark Hanly; Emmanuel Mnatzaganian; Brandon Hao; Marcus Di Sipio; Guolin Yu; Jash Vanjara; Ivy Cerelia Valerie; Juliana de Oliveira Costa; Timothy Churches; Sanja Lujic; et al. (JMIR Medical Education, 2024)
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.