摘要
目的利用数据挖掘方法探究周平安教授治疗肺纤维化的组方用药规律和经验方。方法筛选整理周平安教授治疗肺纤维化医案,分别利用Clementine 12.0和SPSS 21.0软件对高频药物进行性味归经分析、关联规则及聚类分析。结果(1)药物及性味归经分析总结出清热药、补虚药、止咳化痰平喘药位居前三,性温平、味甘苦辛、入肺经药物使用频次较高;(2)关联规则提取出常用药对及药物组合为生黄芪、金银花、穿山龙、石韦、当归中两药或多种药的组合;(3)聚类分析得出四个常用药物组合,其中之一便是周教授经验方“肺痹汤”的基本药物组成。结论周平安教授临床治疗肺纤维化用药平和,所用核心药物组合恰为经验方肺痹汤的组成,与前期理论相吻合。
Objective To explore the patterns of the composition and prescription of traditional Chinese medicine(TCM)of professor Zhou Ping-an in the treatment of pulmonary fibrosis.Methods The medical records of professor Zhou Ping-an used for treating pulmonary fibrosis were filtered and reorganized,frequency analysis,association rule analysis and cluster analysis were used to analyse flavor and channel tropism of the herbs prescribed.Tthe analysis software were Clementine 12.0 and SPSS 21.0.Results(1)The results of frequency analysis showed that heat-clearing,deficiency-nourishing and cough-relieving and sputum-resolving and asthma-soothing herbs were at the top three which were most commonly used.(2)Association rule analysis extracted frequently used pairs of herbs and frequently used compositions of herbs,which were the combination of two or more Chinese medicines of astragalus membranaceus,honeysuckle,rhizoma dioscoreae nipponicae,pyrrosia lingua and angelica sinensis.(3)Cluster analysis obtained four clusters of herbs,and one of them was the basic herb composition of Professor Zhou’s empirical prescription Feibi decoction.Conclusion Professor Zhou Ping-an’s clinical treatment prescription of pulmonary fibrosis was peaceful,and the core herb combination was exactly the empirical formula Feibi decoction,which was consistent with the previous theory.
作者
李国栋
李改杰
李丽
焦扬
LI Guodong;LI Gaijie;LI Li;JIAO Yang(Respiratory Department of Beijing Changping Hospital of Integrated Chinese and Western Medicine,Beijing 102208,China)
出处
《环球中医药》
CAS
2023年第7期1333-1339,共7页
Global Traditional Chinese Medicine
基金
国家中医药管理局全国名老中医药专家传承工作室建设项目
北京市中医管理局薪火传承周平安名医工作站建设项目。
关键词
周平安
肺纤维化
数据挖掘
用药规律
Zhou Ping-an
Pulmonary fibrosis
Data mining
Prescription patterns
