Generate medical synthetic data based on generative adversarial network
Modeling the probability distribution of rows in structured electronic health records and generating realistic synthetic data is a non-trivial task.Tabular data usually contains discrete columns, and traditional encoding approaches may suffer from the curse of feature dimensionality.Poincaré Ball mo...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2022-03-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022057/ |
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| _version_ | 1841539952090808320 |
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| author | Xiayu XIANG Jiahui WANG Zirui WANG Shaoming DUAN Hezhong PAN Rongfei ZHUANG Peiyi HAN Chuanyi LIU |
| author_facet | Xiayu XIANG Jiahui WANG Zirui WANG Shaoming DUAN Hezhong PAN Rongfei ZHUANG Peiyi HAN Chuanyi LIU |
| author_sort | Xiayu XIANG |
| collection | DOAJ |
| description | Modeling the probability distribution of rows in structured electronic health records and generating realistic synthetic data is a non-trivial task.Tabular data usually contains discrete columns, and traditional encoding approaches may suffer from the curse of feature dimensionality.Poincaré Ball model was utilized to model the hierarchical structure of nominal variables and Gaussian copula-based generative adversarial network was employed to provide synthetic structured electronic health records.The generated training data are experimentally tested to achieve only 2% difference in utility from the original data yet ensure privacy. |
| format | Article |
| id | doaj-art-02ce4aa85dbe43358fb9a366150985e9 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2022-03-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-02ce4aa85dbe43358fb9a366150985e92025-01-14T06:29:12ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2022-03-014321122459393234Generate medical synthetic data based on generative adversarial networkXiayu XIANGJiahui WANGZirui WANGShaoming DUANHezhong PANRongfei ZHUANGPeiyi HANChuanyi LIUModeling the probability distribution of rows in structured electronic health records and generating realistic synthetic data is a non-trivial task.Tabular data usually contains discrete columns, and traditional encoding approaches may suffer from the curse of feature dimensionality.Poincaré Ball model was utilized to model the hierarchical structure of nominal variables and Gaussian copula-based generative adversarial network was employed to provide synthetic structured electronic health records.The generated training data are experimentally tested to achieve only 2% difference in utility from the original data yet ensure privacy.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022057/generative adversarial networkrepresentation learningprivacy-utility analysiselectronic health record |
| spellingShingle | Xiayu XIANG Jiahui WANG Zirui WANG Shaoming DUAN Hezhong PAN Rongfei ZHUANG Peiyi HAN Chuanyi LIU Generate medical synthetic data based on generative adversarial network Tongxin xuebao generative adversarial network representation learning privacy-utility analysis electronic health record |
| title | Generate medical synthetic data based on generative adversarial network |
| title_full | Generate medical synthetic data based on generative adversarial network |
| title_fullStr | Generate medical synthetic data based on generative adversarial network |
| title_full_unstemmed | Generate medical synthetic data based on generative adversarial network |
| title_short | Generate medical synthetic data based on generative adversarial network |
| title_sort | generate medical synthetic data based on generative adversarial network |
| topic | generative adversarial network representation learning privacy-utility analysis electronic health record |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022057/ |
| work_keys_str_mv | AT xiayuxiang generatemedicalsyntheticdatabasedongenerativeadversarialnetwork AT jiahuiwang generatemedicalsyntheticdatabasedongenerativeadversarialnetwork AT ziruiwang generatemedicalsyntheticdatabasedongenerativeadversarialnetwork AT shaomingduan generatemedicalsyntheticdatabasedongenerativeadversarialnetwork AT hezhongpan generatemedicalsyntheticdatabasedongenerativeadversarialnetwork AT rongfeizhuang generatemedicalsyntheticdatabasedongenerativeadversarialnetwork AT peiyihan generatemedicalsyntheticdatabasedongenerativeadversarialnetwork AT chuanyiliu generatemedicalsyntheticdatabasedongenerativeadversarialnetwork |