You Mingyu, Chen Chong, Li Guo-Zheng, Yan Shi-Xing, Sun Sheng, Zeng Xue-Qiang, Zhao Qing-Ce, Xu Liao-Yu, Huang Su-Ying
Department of Control Science and Engineering, Tongji University, Shanghai 200000, China.
Computer Center, Nanchang University, Nanchang 330000, China.
ScientificWorldJournal. 2015;2015:473168. doi: 10.1155/2015/473168. Epub 2015 Oct 1.
Clinical cases are primary and vital evidence for Traditional Chinese Medicine (TCM) clinical research. A great deal of medical knowledge is hidden in the clinical cases of the highly experienced TCM practitioner. With a deep Chinese culture background and years of clinical experience, an experienced TCM specialist usually has his or her unique clinical pattern and diagnosis idea. Preserving huge clinical cases of experienced TCM practitioners as well as exploring the inherent knowledge is then an important but arduous task. The novel system ISMAC (Intelligent System for Management and Analysis of Clinical Cases in TCM) is designed and implemented for customized management and intelligent analysis of TCM clinical data. Customized templates with standard and expert-standard symptoms, diseases, syndromes, and Chinese Medince Formula (CMF) are constructed in ISMAC, according to the clinical diagnosis and treatment characteristic of each TCM specialist. With these templates, clinical cases are archived in order to maintain their original characteristics. Varying data analysis and mining methods, grouped as Basic Analysis, Association Rule, Feature Reduction, Cluster, Pattern Classification, and Pattern Prediction, are implemented in the system. With a flexible dataset retrieval mechanism, ISMAC is a powerful and convenient system for clinical case analysis and clinical knowledge discovery.
临床病例是中医临床研究的首要和关键证据。大量医学知识蕴含在经验丰富的中医从业者的临床病例中。凭借深厚的中国文化背景和多年临床经验,经验丰富的中医专家通常有其独特的临床模式和诊断思路。因此,保存经验丰富的中医从业者的大量临床病例并挖掘其中的内在知识是一项重要而艰巨的任务。新型系统ISMAC(中医临床病例管理与分析智能系统)专为中医临床数据的定制管理和智能分析而设计和实现。根据每位中医专家的临床诊疗特点,在ISMAC中构建了包含标准和专家标准症状、疾病、证候及中药方剂的定制模板。利用这些模板,临床病例得以存档以保持其原始特征。系统中实施了多种数据分析和挖掘方法,分为基础分析、关联规则、特征约简、聚类、模式分类和模式预测。凭借灵活的数据集检索机制,ISMAC是一个用于临床病例分析和临床知识发现的强大且便捷的系统。