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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 1 of 1 Research Studies DisplayedGates A, Guitard S, Pillay J
Performance and usability of machine learning for screening in systematic reviews: a comparative evaluation of three tools.
Researchers explored the performance of three machine learning tools designed to facilitate title and abstract screening in systematic reviews (SRs) when used to eliminate irrelevant records and complement the work of a single reviewer. Using Abstrackr, DistillerSR, and RobotAnalyst, they found that the workload savings afforded in the automated simulation came with increased risk of missing relevant records. Supplementing a single reviewer's decisions with relevance predictions sometimes reduced the proportion missed, but performance varied by tool and SR. They recommend designing tools based on reviewers' self-identified preferences to improve compatibility with present workflows.
AHRQ-funded; 290201500001I.
Citation: Gates A, Guitard S, Pillay J .
Performance and usability of machine learning for screening in systematic reviews: a comparative evaluation of three tools.
Syst Rev 2019 Nov 15;8(1):278. doi: 10.1186/s13643-019-1222-2..
Keywords: Patient-Centered Outcomes Research, Health Services Research (HSR), Research Methodologies, Evidence-Based Practice, Comparative Effectiveness