Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review.

Journal: BMJ (Clinical research ed.)
Published Date:

Abstract

OBJECTIVE: To assess the methodological quality of studies on prediction models developed using machine learning techniques across all medical specialties.

Authors

  • Constanza L Andaur Navarro
    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands c.l.andaurnavarro@umcutrecht.nl.
  • Johanna A A Damen
    Julius Centre for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
  • Toshihiko Takada
    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Steven W J Nijman
    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Paula Dhiman
    Center for Statistics in Medicine, University of Oxford, Oxford, UK.
  • Jie Ma
    Respiratory Department, Beijing Hospital of Integrated Traditional Chinese and Western Medicine, Beijing, China.
  • Gary S Collins
    Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Windmill Road, Oxford, OX3 7LD UK; Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
  • Ram Bajpai
    School of Primary, Community and Social Care, Keele University, Keele, UK.
  • Richard D Riley
    School of Primary, Community and Social Care, Keele University, Keele, UK.
  • Karel G M Moons
    Julius Center for Health Sciences and Primary Care, and Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
  • Lotty Hooft
    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.