Automatic evidence quality prediction to support evidence-based decision making.

Journal: Artificial intelligence in medicine
Published Date:

Abstract

BACKGROUND: Evidence-based medicine practice requires practitioners to obtain the best available medical evidence, and appraise the quality of the evidence when making clinical decisions. Primarily due to the plethora of electronically available data from the medical literature, the manual appraisal of the quality of evidence is a time-consuming process. We present a fully automatic approach for predicting the quality of medical evidence in order to aid practitioners at point-of-care.

Authors

  • Abeed Sarker
    Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.
  • Diego Mollá
    Department of Computing, Macquarie University, Sydney, NSW 2109, Australia.
  • Cécile Paris
    Commonwealth Scientific and Industrial Research Organisation, Crn Vimiera and Pembroke Roads, Marsfield, NSW 2122, Australia.