Semi-automated screening of biomedical citations for systematic reviews View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2010-12

AUTHORS

Byron C Wallace, Thomas A Trikalinos, Joseph Lau, Carla Brodley, Christopher H Schmid

ABSTRACT

BACKGROUND: 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. 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. RESULTS: We 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. CONCLUSIONS: We 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. More... »

PAGES

55

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-11-55

DOI

http://dx.doi.org/10.1186/1471-2105-11-55

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1050900622

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/20102628


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