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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Vydané v:	Journal of the American Medical Informatics Association : JAMIA Ročník 27; číslo 9; s. 1411
Hlavní autori:	Lee, Dongha, Yu, Hwanjo, Jiang, Xiaoqian, Rogith, Deevakar, Gudala, Meghana, Tejani, Mubeen, Zhang, Qiuchen, Xiong, Li
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England 01.09.2020
Predmet:	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
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