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A Multifaceted benchmarking of synthetic electronic health record generation models

Author

Listed:
  • 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
    Tempus Labs)

  • Sean D. Mooney

    (University of Washington)

  • Bradley A. Malin

    (Vanderbilt University Medical Center
    Vanderbilt University
    Vanderbilt University Medical Center)

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.

Suggested Citation

  • Chao Yan & Yao Yan & Zhiyu Wan & Ziqi Zhang & Larsson Omberg & Justin Guinney & Sean D. Mooney & Bradley A. Malin, 2022. "A Multifaceted benchmarking of synthetic electronic health record generation models," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35295-1
    DOI: 10.1038/s41467-022-35295-1
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    References listed on IDEAS

    as
    1. Chunhui Yuan & Haitao Yang, 2019. "Research on K-Value Selection Method of K-Means Clustering Algorithm," J, MDPI, vol. 2(2), pages 1-10, June.
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    Cited by:

    1. Qi Chang & Zhennan Yan & Mu Zhou & Hui Qu & Xiaoxiao He & Han Zhang & Lohendran Baskaran & Subhi Al’Aref & Hongsheng Li & Shaoting Zhang & Dimitris N. Metaxas, 2023. "Mining multi-center heterogeneous medical data with distributed synthetic learning," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    2. Brandon Theodorou & Cao Xiao & Jimeng Sun, 2023. "Synthesize high-dimensional longitudinal electronic health records via hierarchical autoregressive language model," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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