Creating efficiencies in the extraction of data from randomized trials: a prospective evaluation of a machine learning and text mining tool.

Journal: BMC medical research methodology
Published Date: Aug 16, 2021

Abstract

BACKGROUND: Machine learning tools that semi-automate data extraction may create efficiencies in systematic review production. We evaluated a machine learning and text mining tool's ability to (a) automatically extract data elements from randomized trials, and (b) save time compared with manual extraction and verification.

Authors

  • Allison Gates
    Alberta Research Centre for Health Evidence (ARCHE), Department of Pediatrics, University of Alberta, 4-472, Edmonton Clinic Health Academy, 11405-87 Avenue NW, Edmonton, Alberta T6G 1C9, Canada.
  • Michelle Gates
    Alberta Research Centre for Health Evidence, Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada.
  • Shannon Sim
    Department of Pediatrics and the Alberta Research Centre for Health Evidence, University of Alberta, Edmonton Clinic Health Academy, 11405 87 Avenue NW, Edmonton, Alberta, T6G 1C9, Canada.
  • Sarah A Elliott
    Department of Pediatrics, Alberta Research Centre for Health Evidence and the University of Alberta Evidence-based Practice Center, University of Alberta, 11405 87 Ave NW, Edmonton, Alberta, T6G 1C9, Canada.
  • Jennifer Pillay
    Department of Pediatrics, Alberta Research Centre for Health Evidence and the University of Alberta Evidence-based Practice Center, University of Alberta, 11405 87 Ave NW, Edmonton, Alberta, T6G 1C9, Canada.
  • Lisa Hartling
    Alberta Research Centre for Health Evidence (ARCHE), Department of Pediatrics, University of Alberta, 4-472, Edmonton Clinic Health Academy, 11405-87 Avenue NW, Edmonton, Alberta T6G 1C9, Canada. Electronic address: hartling@ualberta.ca.

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