Semi-automated screening of biomedical citations for systematic reviews.

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-autom

SUBMITTER: Wallace BC 

PROVIDER: S-EPMC2824679 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature