Automatic classification of sentences to support Evidence Based Medicine

TLDR

The study aims to automatically annotate sentences in medical abstracts with predefined Evidence Based Medicine categories. The authors built a 1,000‑abstract corpus annotated with medical categories and used Conditional Random Fields with lexical, semantic, structural, and sequential features to classify sentences. The CRF model achieved micro‑averaged F‑scores of 80.9%/66.9% on structured/unstructured abstracts and 89.3%/74.0% on key sentences, with best performance using unigrams, section headings, and sequential features, outperforming prior methods. The study references standard medical categories such as Intervention and Outcome.

Abstract

Abstract 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 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. Results For 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). Conclusions Of 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.