Applications of machine learning approaches for pediatric asthma exacerbation management: a systematic review.

Journal: BMC medical informatics and decision making
PMID: 40251545

Abstract

BACKGROUND: Pediatric asthma is a common chronic respiratory disease worldwide, and its acute exacerbation events significantly impact children's health and quality of life. Machine learning, an advanced data analysis technique, has shown great potential in healthcare applications in recent years. This systematic review aims to assess the application of ML techniques in pediatric asthma exacerbation and explore their effectiveness and potential value.

Authors

  • Chunni Zhou
    School of Public Health, Southeast University, 87, Dingjiaqiao Road, Gulou District, Nanjing, 210009, China.
  • Liu Shuai
    School of Public Health, Southeast University, 87, Dingjiaqiao Road, Gulou District, Nanjing, 210009, China.
  • Hao Hu
    Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
  • Carolina Oi Lam Ung
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China.
  • Yunfeng Lai
    School of Public Health and Management, Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Lijun Fan
    School of Public Health, Southeast University, 87, Dingjiaqiao Road, Gulou District, Nanjing, 210009, China.
  • Wei Du
    Department of Respiratory and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
  • Yan Wang
    College of Animal Science and Technology, Beijing University of Agriculture, Beijing, China.
  • Meng Li
    Co-Innovation Center for the Sustainable Forestry in Southern China; Cerasus Research Center; College of Biology and the Environment, Nanjing Forestry University, Nanjing, China.

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