Does machine learning have a role in the prediction of asthma in children?

Journal: Paediatric respiratory reviews
Published Date: Jun 9, 2021

Abstract

Asthma is the most common chronic lung disease in childhood. There has been a significant worldwide effort to develop tools/methods to identify children's risk for asthma as early as possible for preventative and early management strategies. Unfortunately, most childhood asthma prediction tools using conventional statistical models have modest accuracy, sensitivity, and positive predictive value. Machine learning is an approach that may improve on conventional models by finding patterns and trends from large and complex datasets. Thus far, few studies have utilized machine learning to predict asthma in children. This review aims to critically assess these studies, describe their limitations, and discuss future directions to move from proof-of-concept to clinical application.

Authors

  • Dimpalben Patel
    Wal-yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, Australia; School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, Australia. Electronic address: Dimpal.Patel@telethonkids.org.au.
  • Graham L Hall
    Wal-yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, Australia; School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, Australia. Electronic address: Graham.Hall@telethonkids.org.au.
  • David Broadhurst
    Centre for Integrative Metabolomics & Computational Biology, Edith Cowan University, Joondalup, Australia. Electronic address: D.broadhurst@ecu.edu.au.
  • Anne Smith
    School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, Australia. Electronic address: Anne.Smith@curtin.edu.au.
  • AndrĂ© Schultz
    Wal-yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, Australia; Department of Respiratory Medicine, Child and Adolescent Health Service, Perth, Australia; Division of Paediatrics, Faculty of Medicine, University of Western Australia, Perth, Australia. Electronic address: Andre.schultz@health.wa.gov.au.
  • Rachel E Foong
    Wal-yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, Australia; School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, Australia. Electronic address: Rachel.Foong@telethonkids.org.au.

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