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, DistillerSR, and RobotAnalyst, respectively, the median (range) proportion missed was 5 (0 to 28) percent, 97 (96 to 100) percent, and 70 (23 to 100) percent for the automated simulation and 1 (0 to 2) percent, 2 (0 to 7) percent, and 2 (0 to 4) percent for the semi-automated simulation. The median (range) workload savings was 90 (82 to 93) percent, 99 (98 to 99) percent, and 85 (85 to 88) percent for the automated simulation and 40 (32 to 43) percent, 49 (48 to 49) percent, and 35 (34 to 38) percent for the semi-automated simulation. The median (range) time savings was 154 (91 to 183), 185 (95 to 201), and 157 (86 to 172) hours for the automated simulation and 61 (42 to 82), 92 (46 to 100), and 64 (37 to 71) hours for the semi-automated simulation. Abstrackr identified 33-90% of records missed by a single reviewer. RobotAnalyst performed less well and DistillerSR provided no relative advantage. User experiences depended on user friendliness, qualities of the user interface, features and functions, trustworthiness, ease and speed of obtaining predictions, and practicality of the export file(s).

Conclusions: 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 (semi-automated simulation) sometimes reduced the proportion missed, but performance varied by tool and SR. Designing tools based on reviewers' self-identified preferences may improve their compatibility with present workflows.

Systematic review registration: Not applicable.

Keywords: Automation; Machine learning; Systematic reviews; Usability; User experience.

Fig. 1

Proportion missed (percent) by tool and systematic review, automated simulation

Fig. 2

Workload savings (percent) by tool and systematic review, automated simulation

Fig. 3

Estimated time savings (days) by tool and systematic review, automated simulation

Fig. 4

Proportion missed (percent) by tool and systematic review, semi-automated simulation

Fig. 5

Workload savings (percent) by tool and systematic review, semi-automated simulation

Fig. 6

Estimated time savings (days) by tool and systematic review, semi-automated simulation