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19(01) 16-24
Comparison of Machine Learning Algorithms for Classification of the Sentences in Three Clinical Practice Guidelines
Mi Hwa Song, PhD1, Young Ho Lee, PhD2, Un Gu Kang, PhD2
1Information and Communication Science, Semyung University, Jecheon; 2IT Department, Gachon University, Incheon, Korea
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Objectives: Clinical Practice Guidelines (CPGs) are an effective tool for minimizing the gap between a physician's clinical decision and medical evidence and for modeling the systematic and standardized pathway used to provide better medical treatment to patients. Methods: In this study, sentences within the clinical guidelines are categorized according to a classification system. We used three clinical guidelines that incorporated knowledge from medical experts in the field of family medicine. These were the seventh report of the Joint National Committee (JNC7) on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure from the National Heart, Lung, and Blood Institute; the third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults from the same institution; and the Standards of Medical Care in Diabetes 2010 report from the American Diabetes Association. Three annotators each tagged 346 sentences hand-chosen from these three clinical guidelines. The three annotators then carried out cross-validations of the tagged corpus. We also used various machine learning-based classifiers for sentence classification. Results: We conducted experiments using real-valued features and token units, as well as a Boolean feature. The results showed that the combination of maximum entropy-based learning and information gain-based feature extraction gave the best classification performance (over 98% f-measure) in four sentence categories. Conclusions: This result confirmed the contribution of the feature reduction algorithm and optimal technique for very sparse feature spaces, such as the sentence classification problem in the clinical guideline document.
Healthcare Informatics Research 2013 Mar; 19(01) 16-24
Keyword : Knowledge Bases, Data Mining, Information Storage and Retrieval

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