A Multifaceted benchmarking of synthetic electronic health record generation models

Chao Yan, Yao Yan, Zhiyu Wan, Ziqi Zhang, Larsson Omberg, Justin Guinney, Sean D. Mooney () and Bradley A. Malin ()
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Chao Yan: Vanderbilt University Medical Center
Yao Yan: Sage Bionetworks
Zhiyu Wan: Vanderbilt University Medical Center
Ziqi Zhang: Vanderbilt University
Larsson Omberg: Sage Bionetworks
Justin Guinney: University of Washington
Sean D. Mooney: University of Washington
Bradley A. Malin: Vanderbilt University Medical Center

Nature Communications, 2022, vol. 13, issue 1, 1-18

Abstract: 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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35295-1

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DOI: 10.1038/s41467-022-35295-1

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