A statistical relational learning approach to identifying evidence based medicine categories

Authors:
Mathias Verbeke;Vincent Van Asch;Roser Morante;Paolo Frasconi;Walter Daelemans;Luc De Raedt
Affiliations:
Katholieke Universiteit Leuven, Belgium;Universiteit Antwerpen, Belgium;Universiteit Antwerpen, Belgium;Università degli Studi di Firenze, Italy;Universiteit Antwerpen, Belgium;Katholieke Universiteit Leuven, Belgium
Venue:
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Year:
2012

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Abstract

Evidence-based medicine is an approach whereby clinical decisions are supported by the best available findings gained from scientific research. This requires efficient access to such evidence. To this end, abstracts in evidence-based medicine can be labeled using a set of predefined medical categories, the so-called PICO criteria. This paper presents an approach to automatically annotate sentences in medical abstracts with these labels. Since both structural and sequential information are important for this classification task, we use kLog, a new language for statistical relational learning with kernels. Our results show a clear improvement with respect to state-of-the-art systems.