Application progress of machine learning in patient-ventilator asynchrony during mechanical ventilation: a systematic review.

Journal: Critical care (London, England)
Published Date: Jul 10, 2025

Abstract

INTRODUCTION: Patient-ventilator asynchrony (PVA) is a common and harmful complication during mechanical ventilation, often requiring labor-intensive manual assessment. Machine learning (ML) offers a promising approach for automated and accurate PVA detection and prediction. We conducted a systematic review to evaluate the methodologies and performance of ML models applied to PVA.

Authors

  • Guanxu Jiang
    Wuxi Medical College of Jiangnan University, Wuxi, 214122, China.
  • Jiawei Ma
    State Key Laboratory of Precision Welding & Joining of Materials and Structures, Harbin Institute of Technology, Harbin 150001, China.
  • Hao Xu
    Department of Nuclear Medicine, the First Affiliated Hospital, Jinan University, Guangzhou 510632, P.R.China.gdhyx2012@126.com.
  • Zigang Zhu
    Department of Critical Care Medicine, Jiangnan University Medical Center, Wuxi, 214002, China.
  • Yi Xie
    Department of Plastic Surgery Peninsula Health Melbourne Victoria Australia.
  • Shenhui Ji
    Department of Critical Care Medicine, Jiangnan University Medical Center, Wuxi, 214002, China.
  • Tao Yan
    College of Information Technology and Engineering, Chengdu University, Chengdu, China.
  • Liang Luo
    Department of Critical Care Medicine, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, PR China. Electronic address: luoliang@mail.sysu.edu.cn.

Keywords

External Resources

Popular Topics
Recent Journals