Deep Learning Model for Real-Time Prediction of Intradialytic Hypotension.

Journal: Clinical journal of the American Society of Nephrology : CJASN
PMID: 33574056

Abstract

BACKGROUND AND OBJECTIVES: Intradialytic hypotension has high clinical significance. However, predicting it using conventional statistical models may be difficult because several factors have interactive and complex effects on the risk. Herein, we applied a deep learning model (recurrent neural network) to predict the risk of intradialytic hypotension using a timestamp-bearing dataset.

Authors

  • Hojun Lee
    Department of General Surgery, Armed Forces Capital Hospital, Seongnam, Korea.
  • Donghwan Yun
    Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea.
  • Jayeon Yoo
    Department of Intelligence and Information, Seoul National University, Seoul, Korea.
  • Kiyoon Yoo
    Department of Intelligence and Information, Seoul National University, Seoul, Korea.
  • Yong Chul Kim
    Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea.
  • Dong Ki Kim
    Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea.
  • Kook-Hwan Oh
    Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
  • Kwon Wook Joo
    Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea.
  • Yon Su Kim
    Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea.
  • Nojun Kwak
  • Seung Seok Han
    Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea.

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