Predicting intubation for intensive care units patients: A deep learning approach to improve patient management.

Journal: International journal of medical informatics
PMID: 38554589

Abstract

OBJECTIVE: For patients in the Intensive Care Unit (ICU), the timing of intubation has a significant association with patients' outcomes. However, accurate prediction of the timing of intubation remains an unsolved challenge due to the noisy, sparse, heterogeneous, and unbalanced nature of ICU data. In this study, our objective is to develop a workflow for pre-processing ICU data and to develop a customized deep learning model to predict the need for intubation.

Authors

  • Ruixi Li
    Harbin Institute of Technology Shenzhen, Shenzhen, China. Electronic address: liruixi627@gmail.com.
  • Zenglin Xu
    Big Data Research Center, University of Electronic Science & Technology, Chengdu, Sichuan, China; School of Computer Science and Engineering, University of Electronic Science & Technology, Chengdu, Sichuan, China. Electronic address: zlxu@uestc.edu.cn.
  • Jing Xu
    First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
  • Xinglin Pan
    University of Electronic Science and Technology of China, Chengdu, China; Department of Network Intelligence, Peng Cheng Lab, Shenzhen, China.
  • Hong Wu
    Department of Liver Surgery, Liver Transplantation Division, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
  • Xiaobo Huang
    Intensive Care Unit, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine of University of Electronic Science and Technology, Chengdu, China.
  • Mengling Feng
    Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore.

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