Patient-ventilator asynchrony classification in mechanically ventilated patients: Model-based or machine learning method?

Journal: Computer methods and programs in biomedicine
PMID: 39029417

Abstract

BACKGROUND AND OBJECTIVE: Patient-ventilator asynchrony (PVA) is associated with poor clinical outcomes and remains under-monitored. Automated PVA detection would enable complete monitoring standard observational methods do not allow. While model-based and machine learning PVA approaches exist, they have variable performance and can miss specific PVA events. This study compares a model and rule-based algorithm with a machine learning PVA method by retrospectively validating both methods using an independent patient cohort.

Authors

  • Christopher Yew Shuen Ang
    School of Engineering, Monash University Malaysia, Selangor, Malaysia.
  • Yeong Shiong Chiew
    School of Engineering, Monash University Malaysia, Selangor, Malaysia; Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand. Electronic address: chiew.yeong.shiong@monash.edu.
  • Xin Wang
    Key Laboratory of Bio-based Material Science & Technology (Northeast Forestry University), Ministry of Education, Harbin 150040, China.
  • Ean Hin Ooi
    School of Engineering, Monash University, Sunway, 47500, Malaysia.
  • Matthew E Cove
    Division of Respiratory & Critical Care Medicine, Department of Medicine, National University Health System, Singapore.
  • Yuhong Chen
    Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cellular Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, 361102, China.
  • Cong Zhou
    Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand.
  • J Geoffrey Chase
    Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand.

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