[1]张艺颖 王豆 谭辉 范文涛 王倩.基于数据挖掘分析中医治疗中风后抑郁症配伍规律*[J].现代中医药,2024,(06):040-46.[doi:10.13424/j.cnki.mtcm.2024.06.00]
 ZHANG Yiying WANG Dou TAN Hui FAN Wentao WANG Qian.Analysis of Compatibility Rules of Traditional Chinese MedicineTreatment for Post-Stroke Depression Based on Data Mining[J].Modern Traditional Chinese Medicine,2024,(06):040-46.[doi:10.13424/j.cnki.mtcm.2024.06.00]
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基于数据挖掘分析中医治疗中风后抑郁症配伍规律*()
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《现代中医药》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2024年06期
页码:
040-46
栏目:
出版日期:
2024-11-20

文章信息/Info

Title:
Analysis of Compatibility Rules of Traditional Chinese MedicineTreatment for Post-Stroke Depression Based on Data Mining
文章编号:
1672-0571(2024)04-0041-04
作者:
张艺颖 王豆 谭辉 范文涛 王倩
陕西中医药大学,陕西 咸阳 712046
Author(s):
ZHANG Yiying WANG Dou TAN Hui FAN Wentao WANG Qian
Shaanxi University of Chinese Medicine, Shaanxi Xianyang 712046,China
关键词:
关键词:数据挖掘中医中风后抑郁用药规律补虚化瘀调畅气机
Keywords:
Key words:Data mining Chinese medicine Post stroke depression Medication rules Tonifying deficiency and removing blood stasis Regulating Qi activity
分类号:
R743.3
DOI:
10.13424/j.cnki.mtcm.2024.06.00
文献标志码:
A
摘要:
摘 要:目的 运用数据挖掘方式探讨中医药治疗中风后抑郁(post-stroke depression,PSD)的用药规律,为临床诊疗提供一定的参考价值。方法 检索中国知网(CNKI)、万方数据知识服务平台(万方数据)以及PubMed数据库自2012年7月1—2022年7月31日公开发表的关于中医药治疗PSD的临床研究文献,应用Microsoft Excel 2010建库,使用SPSS Modeler18.0、IBM SPSS Statistics 26.0统计软件进行频次、性味归经、关联、聚类规则分析。结果 符合筛选标准文献95篇,中药134味,总使用频次1092次。其中15味药使用频次≥20次,药性以温、寒、平为主,温最甚;药味以甘、苦、辛为主,其中,甘味最多;归经以归肝、脾、肺、胃、肾、心经为主;使用频次为前5位的中药类别分别为解郁药、补虚药、活血化瘀药、安神药、理气药。运用Apriori算法得到常用关联药对,聚类分析得出8个聚类群。结论 正虚是中风后抑郁的基础,在正虚的基础上伴有“瘀”与“郁”的病机特点,虚、瘀、郁贯穿于病程的各个阶段。临床多以解郁安神、温中补虚、活血行气为治疗原则,治疗多选用补虚药及归肝经的药物,以奏补虚化瘀、调畅气机之功,这为中风后抑郁的中医药治疗方案的制定提供了一定的参考价值。
Abstract:
Abstract:Objective To explore the medication patterns of traditional Chinese medicine in the treatment of post-stroke depression (PSD) using data mining methods, and provide certain reference value for clinical diagnosis and treatment.Methods Retrieve clinical research literature on the treatment of PSD with traditional Chinese medicine published in China National Knowledge Infrastructure (CNKI), Wanfang Data Knowledge Service Platform (Wanfang Data), and PubMed database from July 1, 2012 to July 31, 2022. Use Microsoft Excel 2010 to build the database, and use SPSS Modeler 18.0 and IBM SPSS Statistics 26.0 statistical software for frequency, taste, meridian tropism, association, and clustering rule analysis. Results The results met the screening criteria with 95 articles, 134 traditional Chinese medicines, and a total usage frequency of 1092 times. Among them, 15 medicines have a usage frequency of ≥20 times, and their properties are mainly warm, cold, and mild, with warm being the most severe; The medicinal flavors are mainly sweet, bitter, and spicy, with sweet being the most common; The meridian system mainly includes the liver, spleen, lung, stomach, kidney, and heart meridians; The top 5 categories of traditional Chinese medicine with the highest frequency of use are anti depression drugs, tonifying deficiency drugs, promoting blood circulation and removing blood stasis drugs, calming nerves drugs, and regulating Qi drugs. Using Apriori algorithm to obtain commonly used associated drug pairs, clustering analysis yielded 8 clusters. Conclusion Healthy Qi deficiency is the basis of post-stroke depression, accompanied by the pathological characteristics of “stasis” and “depression” on the basis of Healthy Qi deficiency. Deficiency, stasis, and depression run through various stages of the disease. In clinical practice, the principles of treating depression and calming the mind, warming the middle and tonifying deficiency, and promoting blood circulation and Qi circulation are commonly used. Treatment often involves the use of tonifying deficiency drugs and drugs that return to the liver meridian, in order to promote the functions of tonifying deficiency, removing blood stasis, and regulating Qi flow. This provides a certain reference value for the development of traditional Chinese medicine treatment plans for post-stroke depression.

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备注/Memo

备注/Memo:
基金项目:国家自然科学基金项目(82274332)
更新日期/Last Update: 2024-11-21