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

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作者
Allison Gates
Michelle Gates
Shannon Sim
Sarah A. Elliott
Jennifer Pillay
Lisa Hartling
机构
[1] University of Alberta,Department of Pediatrics and the Alberta Research Centre for Health Evidence
[2] Edmonton Clinic Health Academy,undefined
来源
BMC Medical Research Methodology | / 21卷
关键词
Data collection; Machine learning; Text mining; Efficiency; Systematic reviews; Clinical trials;
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  • [1] Borah R(2017)Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the PROSPERO registry BMJ Open 7 23-30
  • [2] Brown AW(2010)Seventy-five trials and eleven systematic reviews a day: how will we ever keep up? PLoS Med 7 5-12
  • [3] Capers PL(2017)Living systematic review: 1. Introduction—the why, what, when, and how J Clin Epidemiol 91 78-201
  • [4] Kaiser KA(2015)Using text mining for study identification in systematic reviews: a systematic review of current approaches Syst Rev 4 160-244.e37
  • [5] Bastian H(2015)Automating data extraction in systematic reviews: a systematic review Syst Rev 4 74-62
  • [6] Glasziou P(2015)How to conduct systematic reviews more expeditiously? Syst Rev 4 163-298
  • [7] Chalmers I(2014)Systematic review automation technologies Syst Rev 3 7-undefined
  • [8] Elliott JH(2019)Toward systematic review automation: a practical guide to using machine learning tools in research synthesis Syst Rev 8 193-undefined
  • [9] Synnot A(2017)Automating biomedical evidence synthesis: robotreviewer Proc Conf Assoc Comput Linguist Meet 2017 56-undefined
  • [10] Turner T(2015)RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials J Am Med Inform Assoc 23 237-undefined