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.
Frimza et al. REF introduced a per-question classification method that uses an ensemble of classi- fiers that exploit the particular protocol used in creating the systematic review.
Building Systematic Reviews Using Automatic Text Classification Techniques
COLING - POSTERS
Computer Science