Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques.

Journal: BMJ open
Published Date: Nov 11, 2020

Abstract

INTRODUCTION: Studies addressing the development and/or validation of diagnostic and prognostic prediction models are abundant in most clinical domains. Systematic reviews have shown that the methodological and reporting quality of prediction model studies is suboptimal. Due to the increasing availability of larger, routinely collected and complex medical data, and the rising application of Artificial Intelligence (AI) or machine learning (ML) techniques, the number of prediction model studies is expected to increase even further. Prediction models developed using AI or ML techniques are often labelled as a 'black box' and little is known about their methodological and reporting quality. Therefore, this comprehensive systematic review aims to evaluate the reporting quality, the methodological conduct, and the risk of bias of prediction model studies that applied ML techniques for model development and/or validation.

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 G Damen
    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The 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 Gm Moons
    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Lotty Hooft
    Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.

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