基于关联规则与熵聚类的化瘀类中成药组方规律研究  

Analysis on composition rules of stasis-resolving Chinese patent drugs based on association rules and entropy clustering

作　　者：吴嘉瑞[1] 金燕萍[1] 张冰[1] 张月[1] 周唯[1] 

机构地区：[1]北京中医药大学中药学院,北京100012

出　　处：《中国医药导报》2015年第13期117-119,123,共4页China Medical Herald

基　　金：国家科技支撑计划课题(2007BAI10B01);北京市中医药科技项目(JJ-2010-70);北京中医药大学大学生科研项目质量工程项目(BJGJ1420);北京中医药大学与北京灸道堂中医研究院横向合作课题

摘　　要：目的探讨常用化瘀类中成药的组方规律。方法收录《新编国家中成药》中的化瘀类中成药处方,采用关联规则apriori算法和复杂系统熵聚类等方法,确定处方中药物的使用频次及药物之间的关联规则等。结果高频次药物包括丹参、红花、当归、川芎、三七等;高频次药物组合包括"没药,乳香"、"当归,红花"、"川芎,当归"等;置信度较高的关联规则包括"当归,没药→乳香"、"乳香→没药"等。结论处方用药中除常见的化瘀类中药外,尚包括具有补血益气作用的部分补益药、清热凉血药及其他类药物。Objective To investigate composition rules of stasis-resolving Chinese patent drugs.Methods The prescriptions of stasis-resolving Chinese patent drugs in The New National Chinese Patent Drugs were collected to build a database.The methods of association rules with apriori algorithm and complex system entropy clustering were used to achieve the frequency of medicines and association rules between drugs.Results The data-mining results indicated that in the prescriptions of stasis-resolving Chinese patent drugs,the most frequency used drugs were Salvia miltiorrhiza,Carthamus tinctorius,Angelica sinensis,Ligusticum wallichii,Panax notoginseng,and so on.The most frequently drug combinations were ＂myrrh,libanotus＂,＂Angelica sinensis,Carthamus tinctorius＂,＂Ligusticum wallichii,Angelica sinensis＂ and so on.The drugs with a high degree confidence coefficient of association rules included ＂Angelica sinensis,myrrh→libanotus＂,＂libanotus→ myrrh＂.Conclusion From the analysis above,we can find that in the prescriptions of Chinese patent drugs,there are not only stasis-resolving drugs,but also part tonic with blood and Qi,heat clearing drug and other drugs.

关 键 词：化瘀药 关联规则 熵聚类 

分 类 号：R289.5[医药卫生—方剂学]