RESUMEN
BACKGROUND: This study aimed to obtain the symptom, prescription and therapeutic patterns for the treatment of patients with menopausal syndrome in major Korean medicine (KM) hospitals. METHODS: We used a retrospective chart review of climacteric disorder and postmenopausal syndrome patients by examining medical records (ICD-10, menopausal and female climacteric states: N95.1, Menopausal and perimenopausal disorder, unspecified: N95.9) from eight university KM hospitals in South Korea. RESULTS: The main symptoms of 1,682 patients with menopausal disorders visiting eight college-affiliated oriental medicine hospitals were hot flush, hyperhidrosis, fatigue, insomnia, and chest tightness. Guipi decoction, Si-wu guipi decoction, Qing-xin lianzi-yin, Jiawei xiao-yao-san and Guipi wen-dan decoction were the most commonly prescribed treatments for menopausal disorders. Patients were most often treated with a combination of herbal medicine and acupuncture. CONCLUSION: Our study shows that the current prescribed herbal medicines were used for treating menopausal disorders in Korean medicine hospitals. However, the objectivity of the efficacy assessment should be studied further.
RESUMEN
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.
RESUMEN
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.
