Published on in Vol 9, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30401, first published .
Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant Evidence From the Biomedical Literature: Systematic Review

Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant Evidence From the Biomedical Literature: Systematic Review

Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant Evidence From the Biomedical Literature: Systematic Review

Wael Abdelkader   1 * , MD, MSc ;   Tamara Navarro   1 * , MLiS ;   Rick Parrish   1 * , DiplT ;   Chris Cotoi   1 * , BEng, EMBA ;   Federico Germini   1, 2 * , MD, MSc ;   Alfonso Iorio   1, 2 * , MD, PhD, FRCPC ;   R Brian Haynes   1, 2 * , MD, PhD ;   Cynthia Lokker   1 * , MSc, PhD

1 Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada

2 Department of Medicine, McMaster University, Hamilton, ON, Canada

*all authors contributed equally

Corresponding Author:

  • Wael Abdelkader, MD, MSc
  • Health Information Research Unit
  • Department of Health Research Methods, Evidence, and Impact
  • McMaster University
  • 1280 Main St W.
  • CRL Building, First Floor
  • Hamilton, ON, L8S 4K1
  • Canada
  • Phone: 1 647 563 5732
  • Email: Abdelkaw@mcmaster.ca