Comparison of Machine Learning Algorithms for Classification of the Sentences in Three Clinical Practice Guidelines
ObjectivesClinical 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.MethodsIn this study, sentences w...
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| Format: | Article |
| Language: | English |
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The Korean Society of Medical Informatics
2013-03-01
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| Series: | Healthcare Informatics Research |
| Subjects: | |
| Online Access: | http://e-hir.org/upload/pdf/hir-19-16.pdf |
| _version_ | 1798013978824146944 |
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| author | Mi Hwa Song Young Ho Lee Un Gu Kang |
| author_facet | Mi Hwa Song Young Ho Lee Un Gu Kang |
| author_sort | Mi Hwa Song |
| collection | DOAJ |
| description | ObjectivesClinical 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.MethodsIn 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.ResultsWe 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.ConclusionsThis 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. |
| first_indexed | 2024-04-11T15:11:15Z |
| format | Article |
| id | doaj.art-07616f89cc76457dba2c955c341c2418 |
| institution | Directory Open Access Journal |
| issn | 2093-3681 2093-369X |
| language | English |
| last_indexed | 2024-04-11T15:11:15Z |
| publishDate | 2013-03-01 |
| publisher | The Korean Society of Medical Informatics |
| record_format | Article |
| series | Healthcare Informatics Research |
| spelling | doaj.art-07616f89cc76457dba2c955c341c24182022-12-22T04:16:39ZengThe Korean Society of Medical InformaticsHealthcare Informatics Research2093-36812093-369X2013-03-01191162410.4258/hir.2013.19.1.16712Comparison of Machine Learning Algorithms for Classification of the Sentences in Three Clinical Practice GuidelinesMi Hwa Song0Young Ho Lee1Un Gu Kang2Information and Communication Science, Semyung University, Jecheon, Korea.IT Department, Gachon University, Incheon, Korea.IT Department, Gachon University, Incheon, Korea.ObjectivesClinical 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.MethodsIn 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.ResultsWe 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.ConclusionsThis 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.http://e-hir.org/upload/pdf/hir-19-16.pdfknowledge basesdata mininginformation storage and retrieval |
| spellingShingle | Mi Hwa Song Young Ho Lee Un Gu Kang Comparison of Machine Learning Algorithms for Classification of the Sentences in Three Clinical Practice Guidelines Healthcare Informatics Research knowledge bases data mining information storage and retrieval |
| title | Comparison of Machine Learning Algorithms for Classification of the Sentences in Three Clinical Practice Guidelines |
| title_full | Comparison of Machine Learning Algorithms for Classification of the Sentences in Three Clinical Practice Guidelines |
| title_fullStr | Comparison of Machine Learning Algorithms for Classification of the Sentences in Three Clinical Practice Guidelines |
| title_full_unstemmed | Comparison of Machine Learning Algorithms for Classification of the Sentences in Three Clinical Practice Guidelines |
| title_short | Comparison of Machine Learning Algorithms for Classification of the Sentences in Three Clinical Practice Guidelines |
| title_sort | comparison of machine learning algorithms for classification of the sentences in three clinical practice guidelines |
| topic | knowledge bases data mining information storage and retrieval |
| url | http://e-hir.org/upload/pdf/hir-19-16.pdf |
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