Performance and usability of machine learning for screening in systematic reviews: a comparative evaluation of three tools.

ABSTRACT: BACKGROUND:We explored the performance of three machine learning tools designed to facilitate title and abstract screening in systematic reviews (SRs) when used to (a) eliminate irrelevant records (automated simulation) and (b) complement the work of a single reviewer (semi-automated simulation). We evaluated user experiences for each tool. METHODS:We subjected three SRs to two retrospective screening simulations. In each tool (Abstrackr, DistillerSR, RobotAnalyst), we screened a 200-record training set and downloaded the predicted relevance of the remaining records. We calculated the proportion missed and workload and time savings compared to dual independent screening. To test user experiences, eight research staff tried each tool and completed a survey. RESULTS:Using Abstrackr, Distille

SUBMITTER: Gates A 

PROVIDER: S-EPMC6857345 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature