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

Authors

  • Mohamed Yusuf Health Professions, Manchester Metropolitan University, Manchester, UK PubMed articlesGoogle scholar articles
  • Ignacio Atal Centre for Research and Interdisciplinarity (CRI), Université Paris Descartes, Paris, Île-de-France, FranceU1153, 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 PubMed articlesGoogle scholar articles
  • 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 PubMed articlesGoogle scholar articles
  • Philip Smith Health Professions, Manchester Metropolitan University, Manchester, UK PubMed articlesGoogle scholar articles
  • 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 PubMed articlesGoogle scholar articles
  • Martin Fergie Imaging and Data Sciences, The University of Manchester, Manchester, UK PubMed articlesGoogle scholar articles
  • Michael Callaghan Health Professions, Manchester Metropolitan University, Manchester, UK PubMed articlesGoogle scholar articles
  • James Selfe Health Professions, Manchester Metropolitan University, Manchester, UK PubMed articlesGoogle scholar articles
  1. Correspondence to Dr Mohamed Yusuf; m.yusuf{at}mmu.ac.uk
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Citation

Yusuf M, Atal I, Li J, et al
Reporting quality of studies using machine learning models for medical diagnosis: a systematic review

Publication history

  • Received September 27, 2019
  • Revised December 2, 2019
  • Accepted January 13, 2020
  • First published March 23, 2020.
Online issue publication 
March 23, 2020

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