方剂治法模型知识库的扩展设计和建模实验研究  

Expansion Design and Experimental Study on Knowledge Base of the Therapeutic Model for Treatment with Prescriptions of Traditional Chinese Medicine

作　　者：张帆[1] 任廷革[1] 高全泉[2] 刘晓峰[1] 孙燕[1] 陈永义[3] 赵朋娜 

机构地区：[1]北京中医药大学基础医学院,北京100029 [2]中国科学院数学与系统科学研究院,北京100190 [3]中国气象局气象干部培训学院,北京100813

出　　处：《中国中医药信息杂志》2014年第9期13-16,共4页Chinese Journal of Information on Traditional Chinese Medicine

基　　金：国家自然科学基金(81072745);北京中医药大学校级课题(2013-X-039)

摘　　要：目的完善方剂知识发掘的方法,在提高方剂治法模型识别能力的同时,发现影响模型稳健性的关键因素。方法提出中医方剂智能分析系统(CPIAS)知识库的扩展和改进设计,如效-候关系启发式筛选规则的建立,效-素关系、效-素关系鉴别、素-证关系等知识表的创建。在此基础上,CPIAS计算出量化数据,应用中医方剂分类模式识别系统(CPSVM)进行方剂治法建模实验,并对学习结果进行分析。结果利用知识库扩展和改进后的计算结果构成数据样本时,CPSVM机器学习水平明显提升。结论方剂功效筛选、方剂症状排序、方剂证素集合是影响中医方剂治法模型构建质量的关键性因素。Objective To perfect the prescription knowledge discovery methods; To discover the key factors affecting the robustness of prescription therapeutic model as well as improve its recognition capability. Methods Expanded knowledge base and improved design of Chinese Medicine Prescriptions Intelligence Analytic System （CPIAS） were proposed, such as the establishment of the heuristic filtering rules of efficacy-syndrome relationship, knowledge table of efficacy-syndrome element relationship, identification of efficacy-syndrome element relationship, and syndrome element-syndrome relationship. In addition, quantitative data were calculated by CPIAS. Prescription therapeutic modeling experiments on the Chinese medicine prescriptions system were conducted based on support vector machine （CPSVM）, which was also used to analyze the learning outcomes. Results Using expanded knowledge base and improved calculation results can significantly promote learning abilities of CPSVM. Conclusion Screening of efficacies, sorting of symptoms, and collection of syndrome elements are the key factors affecting the quality of prescription therapeutic model.

关 键 词：中医方剂 知识发掘 机器学习 

分 类 号：R205[医药卫生—中医学]