Machine learning for accurate detection of small airway dysfunction-related respiratory changes: an observational study.

Journal: Respiratory research
PMID: 39048993

Abstract

BACKGROUND: The use of machine learning(ML) methods would improve the diagnosis of small airway dysfunction(SAD) in subjects with chronic respiratory symptoms and preserved pulmonary function(PPF). This paper evaluated the performance of several ML algorithms associated with the impulse oscillometry(IOS) analysis to aid in the diagnostic of respiratory changes in SAD. We also find out the best configuration for this task.

Authors

  • Wen-Jing Xu
    Department of Respiratory and Critical Care Medicine, West China School of Medicine and West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China.
  • Wen-Yi Shang
    Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, China.
  • Jia-Ming Feng
    West China School of Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, P. R. China.
  • Xin-Yue Song
    Department of Respiratory and Critical Care Medicine, West China School of Medicine and West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China.
  • Liang-Yuan Li
    Department of Respiratory and Critical Care Medicine, West China School of Medicine and West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China.
  • Xin-Peng Xie
    College of Electrical Engineering and Automation, Sichuan University, Chengdu, 610065, China.
  • Yan-Mei Wang
    ENT Institute and Department of Otorhinolaryngology, Eye and ENT Hospital, Fudan University, Shanghai, China.
  • Bin-Miao Liang
    Department of Respiratory and Critical Care Medicine, West China School of Medicine and West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China. liangbinmiao@163.com.

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