Using a machine learning approach to predict mortality in critically ill influenza patients: a cross-sectional retrospective multicentre study in Taiwan.

Journal: BMJ open
PMID: 32102816

Abstract

OBJECTIVES: Current mortality prediction models used in the intensive care unit (ICU) have a limited role for specific diseases such as influenza, and we aimed to establish an explainable machine learning (ML) model for predicting mortality in critically ill influenza patients using a real-world severe influenza data set.

Authors

  • Chien-An Hu
    Department of Information Engineering and Computer Science, Feng Chia University, Taichung, Taiwan.
  • Chia-Ming Chen
    Department of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan.
  • Yen-Chun Fang
    Department of Management Information Systems, National Chung Hsing University, Taichung, Taiwan.
  • Shinn-Jye Liang
    Division of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan.
  • Hao-Chien Wang
    Division of Chest Medicine, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
  • Wen-Feng Fang
    Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan.
  • Chau-Chyun Sheu
    Division of Pulmonary and Critical Care Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
  • Wann-Cherng Perng
    Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
  • Kuang-Yao Yang
    Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Kuo-Chin Kao
    Department of Thoracic Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan.
  • Chieh-Liang Wu
    Center for Quality Management, Taichung Veterans General Hospital, Taichung, Taiwan.
  • Chwei-Shyong Tsai
    Department of Management Information Systems, National Chung Hsing University, Taichung, Taiwan.
  • Ming-Yen Lin
    Department of Information Engineering and Computer Science, Feng Chia University, Taichung, Taiwan cwc081@hotmail.com linmy@mail.fcu.edu.tw.
  • Wen-Cheng Chao
    Division of Chest, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan cwc081@hotmail.com linmy@mail.fcu.edu.tw.

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