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. 2006 Mar-Apr;13(2):206-19.
doi: 10.1197/jamia.M1929. Epub 2005 Dec 15.

Reducing workload in systematic review preparation using automated citation classification

A M Cohen  1 , W R Hersh, K Peterson, Po-Yin Yen
Affiliations

Reducing workload in systematic review preparation using automated citation classification

A M Cohen et al. J Am Med Inform Assoc. 2006 Mar-Apr.

Abstract

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 three of the topics, the reduction was 50% or greater.

Conclusion: 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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Figures

Figure 1.
Figure 1.
High-level diagram of the overall process.
Figure 2.
Figure 2.
Recall versus w for each of the 15 drug review topics.
Figure 3.
Figure 3.
Work saved over sampling at 95% recall for each of the 15 drug review topics.
See this image and copyright information in PMC

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