Comparison of Machine Learning Algorithms for Classification of the Sentences in Three Clinical Practice Guidelines / 대한의료정보학회지

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.

Medical informatics methods for the clinical evidence extraction

Clinical professionals gain new information to assist in patient care when they read the medical literature. Similarly, in clinical preventive medicine, medical science documents that have previously published can be searched and evaluated in order to confirm the scientific support for the clinical preventive medical service offered in order to prevent chronic disease. This paper introduces the medical informatics techniques for knowledge extraction that can become the basis for clinical practice. Particularly, it discusses the clinical document retrieval and knowledge discovery tools that can search for extracting the knowledge which the medical expert desires with data mining techniques. For example, Clinical medical personnel and medical researchers can locate the information from the latest literature rapidly or find and evaluate the scientific basis for the treatment and prevention of infection. This study can be used when they analyze the correlation between accumulated and different type of data and contributes to the detection of new knowledge. Recently, the concern about the visualization of massive data and information is high as the importance of big data has received greater attention. Contributions to this technique and decision support tools will increase gradually due to the way support for decision-making through scientific evidence for the pattern changing disease is evaluated or as one of the clinical practice guidelines is accepted.

A Multi-Classifier Based Guideline Sentence Classification System / 대한의료정보학회지

OBJECTIVES: An efficient clinical process guideline (CPG) modeling service was designed that uses an enhanced intelligent search protocol. The need for a search system arises from the requirement for CPG models to be able to adapt to dynamic patient contexts, allowing them to be updated based on new evidence that arises from medical guidelines and papers. METHODS: A sentence category classifier combined with the AdaBoost.M1 algorithm was used to evaluate the contribution of the CPG to the quality of the search mechanism. Three annotators each tagged 340 sentences hand-chosen from the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC7) clinical guideline. The three annotators then carried out cross-validations of the tagged corpus. A transformation function is also used that extracts a predefined set of structural feature vectors determined by analyzing the sentential instance in terms of the underlying syntactic structures and phrase-level co-occurrences that lie beneath the surface of the lexical generation event. RESULTS: The additional sub-filtering using a combination of multi-classifiers was found to be more effective than a single conventional Term Frequency-Inverse Document Frequency (TF-IDF)-based search system in pinpointing the page containing or adjacent to the guideline information. CONCLUSIONS: We found that transformation has the advantage of exploiting the structural and underlying features which go unseen by the bag-of-words (BOW) model. We also realized that integrating a sentential classifier with a TF-IDF-based search engine enhances the search process by maximizing the probability of the automatically presented relevant information required in the context generated by the guideline authoring environment.