Generating sequential electronic health records using dual adversarial autoencoder

Recent studies on electronic health records (EHRs) started to learn deep generative models and synthesize a huge amount of realistic records, in order to address significant privacy issues surrounding the EHR. However, most of them only focus on structured records about patients' independent vi...

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Veröffentlicht in:	Journal of the American Medical Informatics Association : JAMIA Jg. 27; H. 9; S. 1411
Hauptverfasser:	Lee, Dongha, Yu, Hwanjo, Jiang, Xiaoqian, Rogith, Deevakar, Gudala, Meghana, Tejani, Mubeen, Zhang, Qiuchen, Xiong, Li
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England 01.09.2020
Schlagworte:	Computer Simulation Confidentiality Electronic Health Records Humans Machine Learning Neural Networks, Computer Software sequential data generation generative autoencoder generative adversarial networks (GANs) electornic health records (EHRs) differential privacy
ISSN:	1527-974X, 1527-974X
Online-Zugang:	Weitere Angaben
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