An explainable artificial intelligence framework for weaning outcomes prediction using features from electrical impedance tomography.

Journal: Computer methods and programs in biomedicine
Published Date: Apr 25, 2025

Abstract

BACKGROUND: Prolonged mechanical ventilation (PMV) might cause ventilator-associated pneumonia and diaphragmatic injury, and may lead to worsening clinical weaning outcomes. The present study proposes a comprehensive machine learning (ML) framework for predicting the weaning outcomes of patients with PMV, without relying on ventilator data, by utilizing features from electrical impedance tomography (EIT).

Authors

  • Pu Wang
    Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
  • Teng-Hui Chen
    Department of Chest Medicine, Far Eastern Memorial Hospital, Chinese Taipei, China.
  • Mei-Yun Chang
    Department of Chest Medicine, Far Eastern Memorial Hospital, Chinese Taipei, China.
  • Hai-Yen Hsia
    Department of Chest Medicine, Far Eastern Memorial Hospital, Chinese Taipei, China.
  • Meng Dai
    Department of Electronic Engineering, Fudan University, Shanghai, 200433, China.
  • Yifan Liu
    College of Orthopedics and Traumatology, Henan University of Chinese Medicine, Zhengzhou, China.
  • Yeong-Long Hsu
    Department of Chest Medicine, Far Eastern Memorial Hospital, Chinese Taipei, China; Department of Healthcare Management, College of Medical Technology and Nursing Yuanpei University of Medical Technology, No. 306 YuanpeiStreet, Hsinchu, Chinese Taipei, China; Department of Electrical Engineering, Yuan Ze University, Taoyuan, Chinese Taipei, China. Electronic address: hsuy10712@gmail.com.
  • Feng Fu
    Department of Mathematics, Dartmouth College, Hanover, NH 03755, USA.
  • Zhanqi Zhao
    Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany.

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