WordNet: a lexical database for English
Communications of the ACM
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
The role of knowledge in conceptual retrieval: a study in the domain of clinical medicine
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Answering Clinical Questions with Knowledge-Based and Statistical Techniques
Computational Linguistics
Natural Language Processing with Python
Natural Language Processing with Python
Clinical information retrieval using document and PICO structure
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Screening nonrandomized studies for medical systematic reviews: A comparative study of classifiers
Artificial Intelligence in Medicine
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Efficient identification of patient, intervention, comparison, and outcome (PICO) components in medical articles is helpful in evidence-based medicine. The purpose of this study is to clarify whether first sentences of these components are good enough to train naive Bayes classifiers for sentence-level PICO element detection. We extracted 19,854 structured abstracts of randomized controlled trials with any P/I/O label from PubMed for naive Bayes classifiers training. Performances of classifiers trained by first sentences of each section (CF) and those trained by all sentences (CA) were compared using all sentences by ten-fold cross-validation. The results measured by recall, precision, and F-measures show that there are no significant differences in performance between CF and CA for detection of O-element (F-measure=0.731+/-0.009 vs. 0.738+/-0.010, p=0.123). However, CA perform better for I-elements, in terms of recall (0.752+/-0.012 vs. 0.620+/-0.007, p