Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Choose Your Words Carefully: An Empirical Study of Feature Selection Metrics for Text Classification
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Naive bayes for text classification with unbalanced classes
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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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.