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基于数据挖掘清代江浙名医与川派名医治疗血证用药对比研究
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Abstract:
目的:基于数据挖掘探讨清代江浙名医治疗血证的用药规律,并与同时期川派治疗血证的代表医家唐容川用药特点进行对比研究。方法:对《清代名医医案精华》所载医案进行收集、整理,采用Microsoft Office Excel 2019建立方药数据库,统计用药频数、药物分类,运用IBM SPSS Statistics 22.0软件对高频药物进行聚类分析,运用IBM SPSS Modeler 18.0 Apriori算法对高频药物进行关联规则分析。结果:共纳入《孟河四家医集》中费伯雄医案、马培之医案、丁甘仁医案、巢崇山医案,《临证指南医案》《湿热病篇》的血证医案,《新安医学名医医案精华》《新安医学内科精华》《新安医集丛刊》血证医案,《钱塘医派》的血证医案中所记载的医案378则,涉及方剂共378首,总用药物频数,其中高频药物(出现频数 ≥ 40) 16种,使用频数最多的是茯苓(207),聚类分析得到有意义的药物聚类4组;关联规则分析得到6组强关联药对。结论:两地医家对血证的病因病机及用药特点上具有共性,也有差异,清代江浙名医治疗血证注重“血病治气”、“见血不止血”,治法以健脾除湿、益气养血、温中止血为主,用药上极具江浙特色,药食同源;后者则以补益气血、疏肝和解为主。两者在血证中对于补益药与止血药的运用是同样看重的。
Objective: Based on data mining to explore the medication rules of famous doctors in Jiangsu and Zhejiang in the Qing Dynasty for treating blood evidence, and to conduct a comparative study with the characteristics of medication used by Tang Rongchuan, a representative doctor of the Chuan school for treating blood evidence in the same period. METHODS: The medical cases contained in the Essence of Medical Cases of Famous Doctors of the Qing Dynasty were collected and organized, and Microsoft Office Excel 2019 was used to establish a database of prescription medicines, statistically count the frequency of medication use, drug classification, cluster analysis of high-frequency medicines by using IBM SPSS statistics 22.0 software, and cluster analysis of high-frequency medicines by using IBM SPSS Modeler 18.0. Apriori algorithm was used to analyze the association rules of high-frequency drugs. Results: A total of 378 medical cases recorded in Fei Boxiong’s medical cases, Ma Peizhi’s medical cases, Ding Ganren’s medical cases, and Chao Chongshan’s medical cases in Menghe Four Medical Collections, the blood evidence medical cases in Clinical Guidelines for Medical Cases, and Damp-Heat Diseases, the blood evidence medical cases in Essentials of Famous Physicians of Hsin-An Medicine, Essentials of Internal Medicine in Hsin-An Medicine, and Hsin-An Medical Collections, the blood evidence medical cases in the Qiantang School of Medical Sciences were incorporated, and the total number of formulas involved was 378. The total frequency of drugs used, of which 16 high-frequency drugs (frequency of occurrence ≥ 40), the most frequently used is Poria (207), cluster analysis obtained 4 groups of meaningful drug clusters; association rule
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