Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 28541493

Abstract

OBJECTIVES: Identifying all published reports of randomized controlled trials (RCTs) is an important aim, but it requires extensive manual effort to separate RCTs from non-RCTs, even using current machine learning (ML) approaches. We aimed to make this process more efficient via a hybrid approach using both crowdsourcing and ML.

Authors

  • Byron C Wallace
    School of Information, University of Texas at Austin, Austin, Texas, USA.
  • Anna Noel-Storr
    Cochrane Dementia and Cognitive Improvement Group University of Oxford United Kingdom.
  • Iain J Marshall
    Department of Primary Care and Public Health Sciences, King's College London, UK iain.marshall@kcl.ac.uk.
  • Aaron M Cohen
    Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA 97239.
  • Neil R Smalheiser
    Department of Psychiatry and Psychiatric Institute, University of Illinois College of Medicine, 1601 West Taylor Street, MC912, Chicago, IL 60612 neils@uic.edu +1-708-312-413-4581.
  • James Thomas
    EPPI-Centre, Social Research Institute, University College London, London, England, UK.

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