Machine learning to help researchers evaluate biases in clinical trials: a prospective, randomized user study.

Journal: BMC medical informatics and decision making
Published Date: May 8, 2019

Abstract

OBJECTIVE: Assessing risks of bias in randomized controlled trials (RCTs) is an important but laborious task when conducting systematic reviews. RobotReviewer (RR), an open-source machine learning (ML) system, semi-automates bias assessments. We conducted a user study of RobotReviewer, evaluating time saved and usability of the tool.

Authors

  • Frank Soboczenski
    School of Population Health & Environmental Sciences, Faculty of Life Sciences and Medicine, King's College London, 3rd Floor, Addison House, Guy's Campus, London, SE1 1UL, UK. frank.soboczenski@kcl.ac.uk.
  • Thomas A Trikalinos
    Center for Evidence Synthesis in Health, Brown University, Providence, USA.
  • Joel Kuiper
    Department of Genetics, Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
  • Randolph G Bias
    School of Information, University of Texas at Austin, Austin, USA.
  • Byron C Wallace
    School of Information, University of Texas at Austin, Austin, Texas, USA.
  • Iain J Marshall
    Department of Primary Care and Public Health Sciences, King's College London, UK iain.marshall@kcl.ac.uk.

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