Reporting quality of studies using machine learning models for medical diagnosis: a systematic review.

Journal: BMJ open
Published Date: Mar 23, 2020

Abstract

AIMS: We conducted a systematic review assessing the reporting quality of studies validating models based on machine learning (ML) for clinical diagnosis, with a specific focus on the reporting of information concerning the participants on which the diagnostic task was evaluated on.

Authors

  • Mohamed Yusuf
    Department of Peace and Development Studies, Njala University, Bo Campus -18, Freetown, Sierra Leone. myusuf@njala.edu.sl.
  • Ignacio Atal
    Centre for Research and Interdisciplinarity (CRI), Université Paris Descartes, Paris, Île-de-France, France.
  • Jacques Li
    U1153, Epidemiology and Biostatistics Sorbonne Paris Cite Research Center (CRESS), Methods of therapeutic evaluation of chronic diseases team (METHODS), INSERM, Université Paris Descartes, Paris, Île-de-France, France.
  • Philip Smith
    Health Professions, Manchester Metropolitan University, Manchester, UK.
  • Philippe Ravaud
    U1153, Epidemiology and Biostatistics Sorbonne Paris Cite Research Center (CRESS), Methods of therapeutic evaluation of chronic diseases team (METHODS), INSERM, Université Paris Descartes, Paris, Île-de-France, France.
  • Martin Fergie
    Imaging and Data Sciences, The University of Manchester, Manchester, UK.
  • Michael Callaghan
    Health Professions, Manchester Metropolitan University, Manchester, UK.
  • James Selfe
    Health Professions, Manchester Metropolitan University, Manchester, UK.

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