A Multifaceted benchmarking of synthetic electronic health record generation models
Synthetic health data have the potential to mitigate privacy concerns when sharing data to support biomedical research and the development of innovative healthcare applications. In this work, the authors introduce a use case oriented benchmarking framework to evaluate data synthesis models through a...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-12-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-022-35295-1 |
| _version_ | 1828169891108093952 |
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| author | Chao Yan Yao Yan Zhiyu Wan Ziqi Zhang Larsson Omberg Justin Guinney Sean D. Mooney Bradley A. Malin |
| author_facet | Chao Yan Yao Yan Zhiyu Wan Ziqi Zhang Larsson Omberg Justin Guinney Sean D. Mooney Bradley A. Malin |
| author_sort | Chao Yan |
| collection | DOAJ |
| description | Synthetic health data have the potential to mitigate privacy concerns when sharing data to support biomedical research and the development of innovative healthcare applications. In this work, the authors introduce a use case oriented benchmarking framework to evaluate data synthesis models through a set of utility and privacy metrics. |
| first_indexed | 2024-04-12T02:59:35Z |
| format | Article |
| id | doaj.art-7f00928d11944926bd2b00764b67250b |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-12T02:59:35Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-7f00928d11944926bd2b00764b67250b2022-12-22T03:50:42ZengNature PortfolioNature Communications2041-17232022-12-0113111810.1038/s41467-022-35295-1A Multifaceted benchmarking of synthetic electronic health record generation modelsChao Yan0Yao Yan1Zhiyu Wan2Ziqi Zhang3Larsson Omberg4Justin Guinney5Sean D. Mooney6Bradley A. Malin7Department of Biomedical Informatics, Vanderbilt University Medical CenterSage BionetworksDepartment of Biomedical Informatics, Vanderbilt University Medical CenterDepartment of Computer Science, Vanderbilt UniversitySage BionetworksDepartment of Biomedical Informatics and Medical Education, University of WashingtonDepartment of Biomedical Informatics and Medical Education, University of WashingtonDepartment of Biomedical Informatics, Vanderbilt University Medical CenterSynthetic health data have the potential to mitigate privacy concerns when sharing data to support biomedical research and the development of innovative healthcare applications. In this work, the authors introduce a use case oriented benchmarking framework to evaluate data synthesis models through a set of utility and privacy metrics.https://doi.org/10.1038/s41467-022-35295-1 |
| spellingShingle | Chao Yan Yao Yan Zhiyu Wan Ziqi Zhang Larsson Omberg Justin Guinney Sean D. Mooney Bradley A. Malin A Multifaceted benchmarking of synthetic electronic health record generation models Nature Communications |
| title | A Multifaceted benchmarking of synthetic electronic health record generation models |
| title_full | A Multifaceted benchmarking of synthetic electronic health record generation models |
| title_fullStr | A Multifaceted benchmarking of synthetic electronic health record generation models |
| title_full_unstemmed | A Multifaceted benchmarking of synthetic electronic health record generation models |
| title_short | A Multifaceted benchmarking of synthetic electronic health record generation models |
| title_sort | multifaceted benchmarking of synthetic electronic health record generation models |
| url | https://doi.org/10.1038/s41467-022-35295-1 |
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