基于中医传承辅助平台挖掘《金匮要略》水气病用药规律

Analysis on Regularity of Prescriptions in "Essentials from the Golden Cabinet" for Water-Qi Disease Based on Traditional Chinese Medical Inheritance Platform

目的:研究《金匮要略》中治疗水气病的用药规律,为临床治疗水气病选方和用药提供参考。方法:收集并筛选《中华医典》中治疗水气病的方剂,应用中医传承辅助平台(V2.5)软件,采用频数分析、关联规则Apriori算法、改进的互信息法等数据挖掘方法对药物配伍规律进行挖掘。结果:共筛选治疗水气病处方101首,涉及107味中药,其中使用频次最高的三位药物为甘草、茯苓、桂枝;选择相关度为7、惩罚度为2,应用复杂系统熵聚类的层次聚类分析演化出3-4味药的核心组合12个,新处方6首。结论:《金匮要略》中治疗水气病以宣肺发汗,健脾利水,温阳散寒为主,反映了“发汗利水、温阳健脾”的祛邪与扶正兼顾之治则治法。

Objective: To analyse the regularities of prescriptions and medication of water-qi disease in"Essentials from the Golden Cabinet"and to provide a reference for clinical selection of medication. Methods: The prescriptions used for water-qi disease in traditional chinese medicine was collected(V5.0). The traditional Chinese medicine inheritance support system(V2.5) was used to build a prescription database of Chinese medicines treating water-qi disease.The software integration data mining method was used to explore the law of drug compatibility according to the frequency analysis and association rules apriori algorithm, improved mutual information method and other data mining methods.Results: After the analysis of 101 prescriptions for water-qi disease which included 107 kinds of herbs in the water-qi disease prescriptions, the most frequently used in"Synopsis of Golden Chamber"were Radix Glycyrrhizae, Poria and Ramulus Cinnamomi;with the 7 of relevancy and 2 of punishment. It was also found that 12 drug combinations within 3-4 herbs which were the most common by the way of hierarchical cluster analysis of complex system entropy clustering. A total of 6 new prescriptions were also picked out. The treatment of water-qi disease in "Essentials from the Golden Cabinet" is based on diffusing the lung, sweating, spleen-invigorating, draining water and warming yang for dispelling cold. It also reflects the basic treatment of "sweating, draining water, warming yang and invigorating the spleen", which means supporting the healthy energy and eliminating the evil factors.