Year: 2022
Type: article
Abstract: Synthetic health data have the potential to mitigate privacy concerns in supporting biomedical research and healthcare applications. Modern approaches for data generation continue to evolve and demonstrate remarkable potential. Yet there is a lack of a systematic assessment framework to benchmark methods as they emerge and determine which methods are most appropriate for which use cases. In this work, we introduce a systematic benchmarking framework to appraise key characteristics with respect to utility and privacy metrics. We apply the framework to evaluate synthetic data generation methods for electronic health records data from two large academic medical centers with respect to several use cases. The results illustrate that there is a utility-privacy tradeoff for sharing synthetic health data and further indicate that no method is unequivocally the best on all criteria in each use case, which makes it evident why synthetic data generation methods need to be assessed in context.
Source: Nature Communications
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $5,890
Funders: U.S. Department of Health and Human Services, National Institute for Health and Care Research, National Institutes of Health, National Institute on Aging, National Human Genome Research Institute, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Center for Advancing Translational Sciences
Awards: G4171111769, AIM-AHEAD Coordinating Center - All Four Cores, Data and Research Support Center , Alzheimer's Disease Research Center, Genetic Privacy and Identity in Community Settings - GetPreCiSe, G9000292904, Vanderbilt Institute for Clinical and Translational Research (VICTR), Institute of Translational Health Sciences, A FAIR Bridge2AI Center (FABRIC), UW Core Center for Clinical Research of Musculoskeletal Conditions
OpenAlex ID: W4310917402