Learning to predict in-hospital mortality risk in the intensive care unit with attention-based temporal convolution network.

Journal: BMC anesthesiology
Published Date: Apr 23, 2022

Abstract

BACKGROUND: Dynamic prediction of patient mortality risk in the ICU with time series data is limited due to high dimensionality, uncertainty in sampling intervals, and other issues. A new deep learning method, temporal convolution network (TCN), makes it possible to deal with complex clinical time series data in ICU. We aimed to develop and validate it to predict mortality risk using time series data from MIMIC III dataset.

Authors

  • Yu-Wen Chen
    Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Science, Chongqing, China.
  • Yu-Jie Li
    Department of Anaesthesiology, Southwest Hospital, The Third Military Medical University (Army Medical University), Chongqing, 400038, China.
  • Peng Deng
    Department of Anaesthesiology, Southwest Hospital, The Third Military Medical University (Army Medical University), Chongqing, 400038, China.
  • Zhi-Yong Yang
    Department of Anaesthesiology, Southwest Hospital, The Third Military Medical University (Army Medical University), Chongqing, 400038, China.
  • Kun-Hua Zhong
    Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu, 610041, China.
  • Li-Ge Zhang
    Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu, 610041, China.
  • Yang Chen
    Orthopedics Department of the First Affiliated Hospital of Tsinghua University, Beijing, China.
  • Hong-Yu Zhi
    Department of Anaesthesiology, Southwest Hospital, The Third Military Medical University (Army Medical University), Chongqing, 400038, China.
  • Xiao-yan Hu
  • Jian-Teng Gu
    Department of Anaesthesiology, Southwest Hospital, The Third Military Medical University (Army Medical University), Chongqing, 400038, China.
  • Jiao-Lin Ning
    Department of Anaesthesiology, Southwest Hospital, The Third Military Medical University (Army Medical University), Chongqing, 400038, China.
  • Kai-Zhi Lu
    Department of Anaesthesiology, Southwest Hospital, The Third Military Medical University (Army Medical University), Chongqing, 400038, China.
  • Ju Zhang
    Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang 330031, China.
  • Zheng-Yuan Xia
    Department of Anaesthesiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
  • Xiao-Lin Qin
    Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu, 610041, China. qinxl2001@126.com.
  • Bin Yi
    Department of Anesthesiology, Southwest Hospital, Third Military Medical University, Chongqing, China.

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