Building Systematic Reviews Using Automatic Text Classification Techniques

The amount of information in medical publications continues to increase at a tremendous rate. Systematic reviews help to process this growing body of information. They are fundamental tools for evidence-based medicine. In this paper, we show that automatic text classification can be useful in building systematic reviews for medical topics to speed up the reviewing process. We propose a per-question classification method that uses an ensemble of classifiers that exploit the particular protocol of a systematic review. We also show that when integrating the classifier in the human workflow of building a review the per-question method is superior to the global method. We test several evaluation measures on a real dataset.

• Type: Journal
• Year: 2010
• Where: COLING
• Authors: Oana Frunza, Diana Inkpen, Stan Matwin
• Keywords: Automatic Text Classification, COLING 2010, Computational Linguistics, Per-question Classification Method, Systematic Review