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. 2021 Apr 1;10(1):93.
doi: 10.1186/s13643-021-01635-3.

Research Screener: a machine learning tool to semi-automate abstract screening for systematic reviews

Kevin E K Chai  1   2 , Robin L J Lines  3 , Daniel F Gucciardi  3 , Leo Ng  4
Affiliations

Research Screener: a machine learning tool to semi-automate abstract screening for systematic reviews

Kevin E K Chai et al. Syst Rev. .

Abstract

Background: Systematic reviews and meta-analyses provide the highest level of evidence to help inform policy and practice, yet their rigorous nature is associated with significant time and economic demands. The screening of titles and abstracts is the most time consuming part of the review process with analysts required review thousands of articles manually, taking on average 33 days. New technologies aimed at streamlining the screening process have provided initial promising findings, yet there are limitations with current approaches and barriers to the widespread use of these tools. In this paper, we introduce and report initial evidence on the utility of Research Screener, a semi-automated machine learning tool to facilitate abstract screening.

Methods: Three sets of analyses (simulation, interactive and sensitivity) were conducted to provide evidence of the utility of the tool through both simulated and real-world examples.

Results: Research Screener delivered a workload saving of between 60 and 96% across nine systematic reviews and two scoping reviews. Findings from the real-world interactive analysis demonstrated a time saving of 12.53 days compared to the manual screening, which equates to a financial saving of USD 2444. Conservatively, our results suggest that analysts who scan 50% of the total pool of articles identified via a systematic search are highly likely to have identified 100% of eligible papers.

Conclusions: In light of these findings, Research Screener is able to reduce the burden for researchers wishing to conduct a comprehensive systematic review without reducing the scientific rigour for which they strive to achieve.

Keywords: Abstract screening; Automation; Machine learning; Systematic reviews.

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Conflict of interest statement

At the time of submission, KC and LN are the developers of the software and receive financial remuneration to maintain the hosting platform and associated requirements to make Research Screener available.

Figures

Fig. 1
Fig. 1
Research Screener assisted screening process. Head symbol = elements that require human input; computer symbol = elements augmented by Research Screener.
Fig. 2
Fig. 2
Research Screener web front end
See this image and copyright information in PMC

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