摘要
目的:运用数据挖掘的方法分析《女科指要》中带下病篇的药物使用规律。方法:使用Excel软件对《女科指要》治疗带下病的方剂进行药物的性味归经及高频药物进行统计;关联规则使用SPSS Modeler 18.0进行分析;高频药物的聚类分析使用SPSS Statistics 25.0。结果:共分析50首方剂,113味中药,药物累计使用频次达332次。高频药物(用药频率 ≥ 5)为白芍、当归、茯苓、黄柏、香附等22味药,其药性多为温性,药味多以甘、辛、苦为主,主归脾、肝、肾、肺经。使用关联规则分析获得具有强关联性的药对包括当归–白芍、当归–川芎、白芍–川芎、白芍–白术。通过聚类分析得到核心药物组合共5组:1) 干姜、柴胡、甘草、黄芩;2) 黄柏、樗白皮、苍术、半夏、香附、白芷;3) 附子、炙甘草;4) 牡蛎、龙骨、五味子;5) 白术、人参、茯苓、当归、川芎、白芍、熟地黄。Objective: To use data mining to analyze the drug use rules of the section of abnormal leukorrhea in Women’s Medical Instruction. Methods: Excel software was used to collect statistics on the sexual and taste normalization and high frequency drugs in the “Female Science Guidelines” for the treatment of abnormal leukorrhea. SPSS Modeler 18.0 software was used for association rule analysis. SPSS Statistics 25.0 software was used for cluster analysis of high frequency drugs. Results: A total of 50 formulas were included, involving 113 Chinese herbal medicines, and the cumulative frequency of use was 332 times. High-frequency drugs (medication frequency ≥ 5) for Paeonia alba, Angelica, Poria, Phellodendron chinensis, Xiangfu and other 22 flavor drugs, its medicinal properties are mostly warm, the taste is mostly sweet, xin, bitter, and the meridians are mainly based on the spleen, liver, kidney and lung meridians. The drug pairs with strong correlation were angelica obtained, including Angelica sinensis-Paeonia alba, Angelica sinensis-Chuanxiong, Paeonia alba-Chuanxiong, and Paeonia alba-Atractylodes macrocephalus. Cluster analysis was used to obtain 5 core drug combinations: five core drug combinations were obtained by cluster analysis:1) Dried ginger, Bupleurum, licorice and Scutellaria;2) Phellodendron chinensis, Chubaipi, Atractylodes lancea, Banxia, Xiangfu, Angelica dahurica;3) Aconite, grilled licorice;4) Oysters, keel, Schisandra;5) Atractylodes macrocephalus, ginseng, Poria, Angelica, Chuanxiong, Paeonia alba, cooked Rehmannia.
出处
《中医学》
2024年第11期2950-2956,共7页
Traditional Chinese Medicine
