摘要
目的:采取数据挖掘技术,探析中药治疗良性阵发性位置性眩晕的组方配伍规律.方法:收集国家知识基础设施数据库(CNKI)、中文科技期刊数据库(CCD)、中国学术期刊数据库(CSPD)、PubMed等数据库中关于中医或中西医治疗良性阵发性位置性眩晕的文献中的内服处方,建立临床方剂数据库,对药物进行频次统计分析、系统聚类分析、关联规则分析及网络可视化分析,以探索中药配伍规律.结果:本研究纳入113篇文献,中药方剂138首,涉及中药127味,其中频次≥10次的中药共有35味.挖掘出核心处方为半夏白术天麻汤合泽泻汤加减方.得到天麻-钩藤、甘草-白芍、半夏-陈皮、泽泻-白术、黄芪-升麻、熟地黄-山药等13对药物配伍.总结出高频药物组合4组.结论:中医治疗良性阵发性位置性眩晕重视肝、脾、肾三脏,标本兼顾,在补益肝脾肾的同时,配伍平肝、清肝、镇肝、祛痰、活血等方法.
Abstract
Objective:To explore the compatibility rules of Chinese medicine prescriptions for benign paroxysmal positional vertigo(BPPV)based on data mining.Methods:The studies about the treatment of BPPV with Chinese medicine alone or combined with Western medicine were searched against the China National Knowledge Infrastructure(CNKI),Chinese Citation Database(CCD),China Science Periodical Database(CSPD),and PubMed.The prescriptions for BPPV were organized into a library,and then fre-quency statistical analysis,hierarchical cluster analysis,association rules analysis,and network visual analysis were carried out to explore the compatibility rules of prescriptions.Results:A total of 113 articles were selected for analysis in this study,which in-volved 138 Chinese medicine prescriptions.The prescriptions involved 127 Chinese medicinal herbs,35 of which had the frequency≥10.The data mining results revealed that modified Banxia Baizhu Tianma Decoction plus Zexie Decoction was the core prescrip-tion.Thirteen herb pairs were obtained,including Gastrodiae Rhizoma-Uncariae Ramulus cum Uncis,Glycyrrhizae Radix et Rhizo-ma-Paeoniae Radix Alba,Pinelliae Rhizoma-Citri Reticulatae Pericarpium,Alismatis Rhizoma-Atractylodis Macrocephalae Rhizo-ma,Astragali Radix-Cimicifugae Rhizoma,and Rehmanniae Radix Praeparata-Dioscoreae Rhizoma.Four combinations of high-fre-quency herbs were summarized.Conclusion:Chinese medicine prescriptions treat BPPV from the liver,spleen,and kidney.In addi-tion to tonifying the liver,spleen,and kidney,the herbs for soothing,clearing,and consolidating the liver,eliminating phlegm,and activating blood were used in the prescriptions.
国家科技期刊平台
NSTL
维普
万方数据