Automatic identification of high impact articles in PubMed to support clinical decision making.

Journal: Journal of biomedical informatics
Published Date: Jul 26, 2017

Abstract

OBJECTIVES: The practice of evidence-based medicine involves integrating the latest best available evidence into patient care decisions. Yet, critical barriers exist for clinicians' retrieval of evidence that is relevant for a particular patient from primary sources such as randomized controlled trials and meta-analyses. To help address those barriers, we investigated machine learning algorithms that find clinical studies with high clinical impact from PubMed®.

Authors

  • Jiantao Bian
    Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
  • Mohammad Amin Morid
    Department of Operations and Information Systems, David Eccles School of Business, University of Utah, Salt Lake City, UT, USA.
  • Siddhartha Jonnalagadda
    Department of Preventive Medicine-Health and Biomedical Informatics, Northwestern University, Chicago, IL, USA.
  • Gang Luo
    Department of Biomedical Informatics and Medical Education, University of Washington UW Medicine South Lake Union, 850 Republican Street, Building C, Box 358047 Seattle, WA 98195, USA, luogang@uw.edu.
  • Guilherme Del Fiol
    Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, United States.

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