Abstract
BackgroundSystematic reviews address a specific clinical question by unbiasedly assessing and analyzing the pertinent literature. Citation screening is a time-consuming and critical step in systematic reviews. Typically, reviewers must evaluate thousands of citations to identify articles eligible for a given review. We explore the application of machine learning techniques to semi-automate citation screening, thereby reducing the reviewers' workload.ResultsWe present a novel online classification strategy for citation screening to automatically discriminate "relevant" from "irrelevant" citations. We use an ensemble of Support Vector Machines (SVMs) built over different feature-spaces (e.g., abstract and title text), and trained interactively by the reviewer(s).Semi-automating the citation screening process is difficult because any such strategy must identify all citations eligible for the systematic review. This requirement is made harder still due to class imbalance; there are far fewer "relevant" than "irrelevant" citations for any given systematic review. To address these challenges we employ a custom active-learning strategy developed specifically for imbalanced datasets. Further, we introduce a novel undersampling technique. We provide experimental results over three real-world systematic review datasets, and demonstrate that our algorithm is able to reduce the number of citations that must be screened manually by nearly half in two of these, and by around 40% in the third, without excluding any of the citations eligible for the systematic review.ConclusionsWe have developed a semi-automated citation screening algorithm for systematic reviews that has the potential to substantially reduce the number of citations reviewers have to manually screen, without compromising the quality and comprehensiveness of the review.
Highlights
Systematic reviews address a specific clinical question by unbiasedly assessing and analyzing the pertinent literature
In this work we focus on pool-based active learning with Support Vector Machines (SVMs)
We report results from experiments conducted over three previously conducted systematic reviews
Summary
Systematic reviews address a specific clinical question by unbiasedly assessing and analyzing the pertinent literature. Citation screening is a time-consuming and critical step in systematic reviews. A systematic review tries to address a well-formulated research question by following a protocol of well-defined steps [1,2]. Systematic reviews appraise and analyze all research reports that fulfill a set of pre-defined eligibility criteria. To identify all eligible reports, reviewers conduct broad searches of the literature, and manually screen the titles and abstracts of all returned citations. All relevant (potentially eligible) citations are retrieved and reviewed in full text to select those that are included in the systematic review
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