摘要
目的探索大肠癌患者中医用药的聚集现象,从中发现和探索治疗大肠癌的组方用药规律。方法通过收集CNKI数据库中有关中医治疗大肠癌的医案文献,检索符合条件的中医药治疗性文献医案137例,采用SPSS 21.0导入数据,采用分析功能中系统聚类法之组间联接法,区间采用Pearson相关性。以高频中药为变量,进行聚类分析和因子分析。结果共筛选出93篇医案文献,137例符合标准的医案,使用中药共203味,药味总频次1760次。在24味高频中药中,其中与气有关的药物有9味,占高频中药总频次的44.79%。聚类分析分为8类,因子分析提取出了8个公因子。结论中药治疗大肠癌有规律可循,辨证用药多以补虚药、清热药为主,配伍严谨。聚类分析和因子分析是研究医案的可行方法,但仍需要更多的对照研究。
Objective To explore the aggregation of TCM for colorectal cancer patients;To discover andexplore the objective compound medication regularities.Methods Medical articles about TCM treatment forcolorectal cancer in CNKI were collected.137medical cases meeting the requirement of TCM treatment wereobtained.SPSS21.0was used to import data.The group connection method of systematic clustering method inanalytic function was used,and Pearson correlation was used in interval.Chinese materia medica with highfrequency was set as variables to conduct clustering and factor analysis.Results93medical records werescreened,with137cases corresponding to the specification,203kinds of Chinese materia meidca,and1760times of frequency.Among the24kinds of high frequency Chinese materia medica,9kinds were related to Qi,accounting for44.8%of the total frequency of high frequency Chinese materia medica.Clustering analysiscould be divided into8types,and8common factors were extracted from factor analysis.Conclusion There areregularities of TCM treatment for colorectal cancer.Syndrome differentiation of diagnosis and prescriptions should mainly use tonic medicine and heat-clearing medicine,with rigorous compatibility.Clustering and factoranalysis are feasible methods to study medical cases,but still need more controlled studies.
作者
莫朵朵
刘秀峰
詹秀菊
MO Duo-duo;LIU Xiu-feng;ZHAN Xiu-ju(School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou Guangdong 510006, China)
出处
《中国中医药图书情报杂志》
2017年第2期12-16,共5页
Chinese Journal of Library and Information Science for Traditional Chinese Medicine
基金
广州中医药大学"薪火计划"资助项目(XH20160105)
关键词
大肠癌
用药规律
聚类分析
因子分析
colorectal cancer
medication regularities
clustering analysis
factor analysis