Automatic classification of sentences to support Evidence Based Medicine

BMC Bioinformatics

Proceedings

Su Nam Kim¹  Lars Yencken¹  David Martinez¹  Lawrence Cavedon² 

[1] NICTA VRL, The University of Melbourne, 3010, Australia;Department of Computer Science and Software Engineering, The University of Melbourne, 3010, Australia;NICTA VRL, The University of Melbourne, 3010, Australia;Department of Computer Science and Software Engineering, The University of Melbourne, 3010, Australia;School of Computer Science and IT, RMIT University, 3000, Melbourne, Australia;

关键词: Semantic Feature;    Conditional Random Field;    Structure Abstract;    Target Sentence;    Unify Medical Language System;   

DOI  :  10.1186/1471-2105-12-S2-S5

来源: Springer

【 摘 要 】

AimGiven a set of pre-defined medical categories used in Evidence Based Medicine, we aim to automatically annotate sentences in medical abstracts with these labels.MethodWe constructed a corpus of 1,000 medical abstracts annotated by hand with specified medical categories (e.g. Intervention, Outcome). We explored the use of various features based on lexical, semantic, structural, and sequential information in the data, using Conditional Random Fields (CRF) for classification.ResultsFor the classification tasks over all labels, our systems achieved micro-averaged f-scores of 80.9% and 66.9% over datasets of structured and unstructured abstracts respectively, using sequential features. In labeling only the key sentences, our systems produced f-scores of 89.3% and 74.0% over structured and unstructured abstracts respectively, 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).ConclusionsOf the features we used, the best for classifying any given sentence in an abstract were based on unigrams, section headings, and sequential information from preceding sentences. These features resulted in improved performance over a simple bag-of-words approach, and outperformed feature sets used in previous work.

【 授权许可】

Unknown   
© Kim et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.