Automatic classification of sentences for evidence based medicine
Su Nam Kim, David Martínez, Lawrence Cavedon · 2010
AIM Given a set of pre-defined medical categories used in Evidence Based Medicine, we aim to automatically annotate sentences in medical abstracts with these labels. METHOD We construct a corpus of 1,000 medical ab-stracts annotated by hand with medical categories (e.g. “In-tervention”, ”Outcome”). We explore the use of various fea-tures based on lexical, semantic, structural, and sequential information in the data, using Conditional Random Fields (CRF) for classification. RESULT For the classification tasks over all labels, our systems achieved micro-averaged F-scores of 80.9 % and 66.9 % in structured and unstructured datasets respectively, using sequential features. In labeling only key sentences, our systems produced F-scores of 89.3% and 74.0 % in structured and unstructured datasets respec-tively, using the same sequential features. The results over an external dataset were lower (F-scores of 63.1 % for all-labels, and 83.8 % for key sentences). CONCLUSION Of the features we used, the best for classifying any given sen-tence in an abstract are based on unigrams, section head-ings, and sequential information from preceding sentences. These features resulted in improved performance over a sim-ple bag-of-words approach, and outperform feature sets used in previous work.