Generating sequential electronic health records using dual adversarial autoencoder.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Jul 1, 2020

Abstract

OBJECTIVE: 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 visits, rather than on chronological clinical records. In this article, we aim to learn and synthesize realistic sequences of EHRs based on the generative autoencoder.

Authors

  • Dongha Lee
    BK21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea; Department of Nuclear Medicine, Department of Radiology, Department of Psychiatry, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea; Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Hwanjo Yu
    Department of Computer Science and Engineering, POSTECH, Pohang, South Korea and.
  • Xiaoqian Jiang
    School of Biomedical Informatics, University of Texas Health, Science Center at Houston, Houston, TX, USA.
  • Deevakar Rogith
    School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA.
  • Meghana Gudala
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX.
  • Mubeen Tejani
    School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA.
  • Qiuchen Zhang
    Department of Computer Science, Emory University, Atlanta, Georgia, USA.
  • Li Xiong
    School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an, 710048, China.

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