Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis.

Journal: The Lancet. Digital health
Published Date:

Abstract

BACKGROUND: Machine learning and deep learning models have been increasingly used to predict long-term disease progression in patients with chronic obstructive pulmonary disease (COPD). We aimed to summarise the performance of such prognostic models for COPD, compare their relative performances, and identify key research gaps.

Authors

  • Luke A Smith
    Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia; School of Public Health, University of Adelaide, Adelaide, SA, Australia. Electronic address: luke.a.smith@adelaide.edu.au.
  • Lauren Oakden-Rayner
    School of Public Health, University of Adelaide, Adelaide, SA, Australia; Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia. Electronic address: lauren.oakden-rayner@adelaide.edu.au.
  • Alix Bird
    Australian Institute for Machine Learning, University of Adelaide, Adelaide, South Australia, Australia.
  • Minyan Zeng
    Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia; School of Public Health, University of Adelaide, Adelaide, SA, Australia.
  • Minh-Son To
    Health and Information, Adelaide, South Australia, Australia; Royal Adelaide Hospital, Adelaide, South Australia, Australia; Flinders Medical Centre, Flinders University, Adelaide, South Australia, Australia.
  • Sutapa Mukherjee
    Department of Respiratory and Sleep Medicine, Southern Adelaide Local Health Network (SALHN), Bedford Park, SA, Australia; Adelaide Institute for Sleep Health/Flinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia.
  • Lyle J Palmer
    School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia.