摘要
目的运用数据挖掘技术探讨朴炳奎教授治疗卵巢癌用药规律。方法选取2015年1月-2021年9月朴炳奎教授门诊卵巢癌患者病历,提取药物处方,采用Excel2019进行药物频次、四气、五味、归经、功效统计,通过SPSS Modeler 18.0软件Apriori算法进行关联规则分析,SPSS Statistics 26.0软件系统聚类功能进行聚类分析,借助Cytoscape3.8.0进行共现网络可视化。结果最终获取中药处方194首,涵盖中药94种,使用总频次4247次。其中以补虚药最多,温性药为主,药味偏甘、辛、苦,归经以脾经、肝经为多。高频药物包括黄芪、枳壳、柴胡、白芍、陈皮、当归等,挖掘得到关联规则21条,聚类组合4类。中成药处方101首,涵盖8种中成药,以软坚消瘤片使用最多。结论朴炳奎教授诊治卵巢癌时时扶正,适时祛邪,以达成安脏腑、调气血、控肿瘤的目标。
Objective To explore the medication law of Professor Piao Bingkui in the treatment of ovarian cancer by data mining technology.Methods The medical record data of ovarian cancer patients in the outpatient of Professor Piao Bingkui from January 2015 to September 2021 were collected,and prescriptions were extracted.Excel 2019 was used for drug frequency,four properties,five flavors,meridian tropism and efficacy statistics.Apriori algorithm in SPSS Modeler 18.0 software was used in analyzing association rules.SPSS Statistics 26.0 system clustering function was used to carry out cluster analysis.And Cytoscape 3.8.0 was used to achieve co-occurrence network visualization.Results A total of 194 prescriptions were included,covering 94 kinds of Traditional Chinese medicine,with the total frequency of 4247 times,among which the tonic drugs were major.Most of the drugs were warm,and the tastes were mainly sweet,pungent and bitter.Most of the meridian tropisms were spleen meridian and liver meridian.High-frequency drugs included Astragali Radix,Aurantii Fructus,Bupleuri Radix,Paeoniae Radix Alba,Citri Reticulatae Pericarpium,Angelicae Sinensis Radix,etc.21 association rules and 4 types of clustering combinations were mined.101 patent medicine prescriptions and 8 Chinese patent medicines were contained,among which the Ruanjian Xiaoliu Pills were used most frequently.Conclusion In the clinical diagnosis and treatment of ovarian cancer,Professor Piao attaches importance to strengthening vital qi constantly and eliminating the pathogenic factors timely,so as to achieve the goal of calming the viscera,regulating qi and blood and controlling tumors.
作者
张兰鑫
陶诗怡
李瑾龙
李铮
樊芬
曹康迪
侯炜
ZHANG Lanxin;TAO Shiyi;LI Jinlong;LI Zheng;FAN Fen;CAO Kangdi;HOUWei(Guang'anmen Hospital,China Academy of Chinese Medical Sciences,Beijing 100053,China;Beijing University of Chinese Medicine,Beijing 100029,China;Beijing Yiqing Tang Traditional Chinese Medicine Clinic,Beijing 100124,China)
出处
《中国中医药信息杂志》
CAS
CSCD
2022年第11期27-32,共6页
Chinese Journal of Information on Traditional Chinese Medicine
基金
中国中医科学院第十三批中医药循证医学项目(ZZ13-024-6)。
关键词
朴炳奎
卵巢癌
关联规则
系统聚类
扶正祛邪
Piao Bingkui
ovarian cancer
association rules
system clustering
strengthening vital qi and eliminate pathogenic factors