中国中药杂志

2012, v.37(21) 3323-3328

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BK算法在中医方剂“基本方”发现中的应用
Application of bron-kerbosch algorithm for discovery of basic formulas of traditional Chinese medicine

杨铭;田雨;陈佳蕾;茅建春;宋瑜;
YANG Ming,TIAN Yu,CHEN Jia-lei,MAO Jian-chun,SONG Yu(Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,Shanghai 200032,China)

摘要(Abstract):

目的:探讨应用BK(Bron-Kerbosch algorithm,BK)算法用于中医方剂"基本方"挖掘的可行性。方法:介绍了BK算法用于"基本方"挖掘的方法及其相关指标,在药物配伍网络的基础上,以"基本方"置信度及其α水平下的支持度为指标,应用BK算法对中医方剂"基本方"进行挖掘,并以龙华医院中医皮肤病专家马绍尧教授的处方为例进行了分析。结果:在优化的参数下,通过BK算法分别得到了3个用于治疗银屑病的"基本方"及4个用于治疗湿疹的"基本方",得到的"基本方"具有较高的置信度水平,基本符合临床实际情况。结论:BK算法可用于中医方剂"基本方"的挖掘,是研究总结名老中医的诊疗经验及用药思路的有效方法之一。
Objective: To study the feasibility of applying the Bron-Kerbosch(BK) algorithm on the discovery of basic formulas(BFs) of traditional Chinese medicine.Method: This essay introduces the BK algorithm for the discovery of BFs of traditional Chinese medicine and relevant indicators.On the basis of the compatibility network of drugs,with the confidence coefficient of basic formulas and the support degree to the decline of α level as indicators,the BK algorithm was used for the discovery of BFs of traditional Chinese medicine.And prescriptions of Professor Ma Shaoyao,a traditional Chinese medicine(TCM) dermatologist of Longhua hospital,were taken as examples for analysis.Result: After the parameters were optimized,three BFs for psoriasis and four BFs for eczema were found using the BK algorithm,which had a relatively high confidence coefficient and basically conformed to the clinical practices.Conclusion: The BK algorithm can be used for the discovery of basic formulas(BF) of traditional Chinese medicine,and therefore it is one of the effective methods to study and summarize the diagnosis and treatment experiences and prescription thoughts of famous and experienced TCM doctors.

关键词(KeyWords): BK算法;复杂网络;“基本方”;数据挖掘
bron-kerbosch algorithm;complex network;basic formula;data mining

Abstract:

Keywords:

基金项目(Foundation): 上海市卫生局科研基金项目(2010Y121);; 上海市医院药学科研基金项目(2010-YY-04)

作者(Author): 杨铭;田雨;陈佳蕾;茅建春;宋瑜;
YANG Ming,TIAN Yu,CHEN Jia-lei,MAO Jian-chun,SONG Yu(Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,Shanghai 200032,China)

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