Machine learning-based model for acute asthma exacerbation detection using routine blood parameters.

Journal: The World Allergy Organization journal
Published Date: Jun 14, 2025

Abstract

BACKGROUND: Acute asthma exacerbations (AAEs) are a leading cause of asthma-related morbidity and mortality, especially in resource-limited settings where pulmonary function tests are unavailable or when patients are unable to cooperate with testing. This study aimed to develop and validate a diagnostic model for AAE using routine blood parameters through machine learning techniques.

Authors

  • Youpeng Chen
    School of Environmental and Ecology, Chongqing University, Chongqing, 400044, China. Electronic address: ypchen@cqu.edu.cn.
  • Junquan Sun
    First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Yabang Chen
    First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Enzhong Li
    First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Jiancai Lu
    Department of Clinical Laboratory, State Key Laboratory of Respiratory Disease, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Huanhua Tang
    Guangzhou Medical University, Guangzhou, China.
  • Yifei Xie
    International School, Jinan University, Guangzhou, China.
  • Jiana Zhang
    Guangzhou Medical University, Guangzhou, China.
  • Lesi Peng
    Guangzhou Medical University, Guangzhou, China.
  • Haojie Wu
    School of Software, Xinjiang University, Urumqi 830046, China.
  • Zhangkai J Cheng
    Department of Clinical Laboratory, National Clinical Research Center of Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou 510120, China.
  • Baoqing Sun
    Department of Clinical Laboratory, National Clinical Research Center of Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou 510120, China.

Keywords

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