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First published December 15, 2005 as JAMIA PrePrint; doi:10.1197/jamia.M1929
Journal of the American Medical Informatics Association 2006;13(2):206-219
© 2006 American Medical Informatics Association

A more recent version of this article appeared on March 1, 2006
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Submitted on August 5, 2005
Accepted on November 30, 2005

Reducing Workload in Systematic Review Preparation Using Automated Citation Classification

A. M. Cohen MD, MS1*, W. R. Hersh MD1, K. Peterson MS1, and Po-Yin Yen MS1

Affiliation of the authors: 1 Department of Medical Informatics, School of Medicine, Oregon Health & Science University, Portland, OR; Clinical Epidemiology, School of Medicine, Oregon Health & Science University, Portland, OR

* To whom correspondence should be addressed.

Objective To determine whether automated classification of document citations can be useful in reducing the time spent by experts reviewing journal articles for inclusion in updating systematic reviews of drug class efficacy for treatment of disease.

Design A test collection was built using the annotated reference files from 15 systematic drug class reviews. A voting perceptron-based automated citation classification system was constructed to classify each article as containing high-quality, drug class-specific evidence or not. Cross-validation experiments were performed to evaluate performance.

Measurements Precision, recall, and F-measure were evaluated at a range of sample weightings. Work saved over sampling at 95% recall was used as the measure of value to the review process.

Results A reduction in the number of articles needing manual review was found for 11 of the 15 drug review topics studied. For 3 of the topics, the reduction was 50% or greater.

Conclusions Automated document citation classification could be a useful tool in maintaining systematic reviews of the efficacy of drug therapy. Further work is needed to refine the classification system and determine the best manner to integrate the system into the production of systematic reviews.






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