Deep learning to refine the identification of high-quality clinical research articles from the biomedical literature: Performance evaluation.

Abstract

Authors

• Cynthia Lokker
  Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada. Electronic address: lokkerc@mcmaster.ca.
• Elham Bagheri
  Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore 639798, Singapore. Electronic address: elham001@e.ntu.edu.sg.
• Wael Abdelkader
  Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
• Rick Parrish
  Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
• Muhammad Afzal
  Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. muhammad.afzal@oslab.khu.ac.kr.
• Tamara Navarro
  Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
• Chris Cotoi
  Health Information Research Unit, McMaster University, Hamilton, ON, Canada.
• Federico Germini
  Health Information Research Unit, Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Department of Medicine, McMaster University, Hamilton, Ontario, Canada.
• Lori Linkins
  Department of Medicine, McMaster University, Hamilton, Ontario, Canada.
• R Brian Haynes
  Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
• Lingyang Chu
  Department of Computing and Software, McMaster University, Hamilton, Ontario, Canada.
• Alfonso Iorio
  Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.

Keywords

External Resources