Semi-automated screening of biomedical citations for systematic reviews

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 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 imba...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Byron C. Wallace, Thomas A. Trikalinos, Joseph Lau, Carla E. Brodley, Christopher H. Schmid
• Keywords: BMCBI 2010, Citation Screening, Specific Clinical Question, Systematic Review