Analysis of medication rules of traditional Chinese medicine in treatment of renal anemia based on data mining

ZHENG Li; LIU Ming; SUN Xuelin

doi:10.6039/j.issn.1001-0408.2023.05.15

您当前的位置：

首页 >

文章列表页 >

Analysis of medication rules of traditional Chinese medicine in treatment of renal anemia based on data mining

更新时间：2023-06-12

- Analysis of medication rules of traditional Chinese medicine in treatment of renal anemia based on data mining
- ZHONGGUO YAOFANG Vol. 34, Issue 5, Pages: 591-594(2023)
- 作者机构：
  
  1.中国航天科工集团七三一医院药学部，北京　100074
  2.兰州大学循证医学中心，兰州　730000
  3.北京医院药学部，北京　100010
- 作者简介：
- 基金信息：
- DOI：10.6039/j.issn.1001-0408.2023.05.15
  CLC： R932;R287
- Published：15 March 2023，
  
  Received：26 August 2022，
  
  Revised：12 January 2023，
扫描看全文
郑丽,刘明,孙雪林.基于数据挖掘的中药治疗肾性贫血的用药规律研究 ^Δ[J].中国药房,2023,34(05):591-594.

ZHENG Li,LIU Ming,SUN Xuelin.Analysis of medication rules of traditional Chinese medicine in treatment of renal anemia based on data mining[J].ZHONGGUO YAOFANG,2023,34(05):591-594.
郑丽,刘明,孙雪林.基于数据挖掘的中药治疗肾性贫血的用药规律研究 ^Δ[J].中国药房,2023,34(05):591-594. DOI： 10.6039/j.issn.1001-0408.2023.05.15.

ZHENG Li,LIU Ming,SUN Xuelin.Analysis of medication rules of traditional Chinese medicine in treatment of renal anemia based on data mining[J].ZHONGGUO YAOFANG,2023,34(05):591-594. DOI： 10.6039/j.issn.1001-0408.2023.05.15.

摘要

目的

对中药治疗肾性贫血的方剂进行数据挖掘，探究中药治疗肾性贫血的用药规律。

方法

通过检索PubMed、the Cochrane Library、Web of Science、中国知网、万方、维普和中国生物医学文献数据库等中英文数据库，筛选中药治疗肾性贫血的相关文献。采用Excel整理并统计所有方药的信息，包括单味中药使用频次、性味、归经、功效；利用R语言对各味中药进行关联规则分析和层次聚类分析。

结果

共纳入文献268篇，涉及中药169味，总使用频次3 919次，其中使用频次≥100次的药物有黄芪、当归、白术、大黄、茯苓、熟地黄、党参、丹参、川芎，药物性味以温药、甘味为主，归经以脾、肝、肾为主，功效以补气、养血、活血化瘀、利水渗湿、攻下为主。关联规则分析结果显示，核心组方为黄芪、当归、白术、茯苓、党参。层次聚类分析结果显示，聚类结果划分为1、2、3层，其中黄芪和当归簇集分类始终相同。

结论

肾性贫血治疗用核心组方为黄芪、当归、白术、茯苓、党参，其中黄芪和当归在核心组方中处于中心位置，不可或缺；用药规律主要以补气、养血、健脾和化湿等为主，可根据患者的不同症状特点辨证论治，在核心组方的基础上加减其他中药。

Abstract

OBJECTIVE

To carry out data mining on the prescriptions of traditional Chinese medicine （TCM） for the treatment of renal anemia， and to explore the medication rules of TCM in the treatment of renal anemia.

METHODS

The Chinese and English databases including PubMed， the Cochrane Library， Web of Science， CNKI， Wanfang， VIP and CBM were searched by computer to screen the related literature about TCM in the treatment of renal anemia. Excel software was used to extract prescription information， including frequency of use， property and flavor， meridian tropism and efficacy of single TCM. Association rule analysis and hierarchical clustering analysis were performed for each ingredient of TCM with R language.

RESULTS

A total of 268 literature were included， involving 169 ingredients with a total use frequency of 3 919 times. Among them，

Astragalus memeranaceus， Angelica sinensis， Atractylodes macrocephala， Rheum officinale， Poria cocos

， Radix Rehmanniae Praeparata， Radix Codonopsis，

Salvia miltiorrhiza

and

Ligusticum chuanxiong

were used more than 100 times. The properties and flavors of the drugs were mainly warm and sweet； meridian tropism mainly targeted spleen， liver and kidney； the function was mainly in replenishing qi， nourishing blood， promoting blood circulation and removing blood stasis， and clearing damp and promoting diuresis， purgation. The result of association rule analysis showed that the core ingredients were

A. memeranaceus， A. sinensis， A. macrocephala， P. cocos

and

S. miltiorrhiza

. Hierarchical cluster analysis showed that the clustering results were divided into grade 1， 2 and 3， and the cluster classification of

A. memeranaceus

and

A. sinensis

was always the same.

CONCLUSIONS

The core prescription for treating renal anemia includes

A. memeranaceus， A. sinensis， A. macrocephala， P. cocos

and

S. miltiorrhiza

； among them，

A. memeranaceus

and

A. sinensis

are at the center of the core prescription and indispensable. The medication rule is mainly guided by drugs for invigorating qi， nourishing blood， strengthening spleen， and removing dampness. According to the characteristics of different symptoms of patients， dialectical treatment is made， and other TCM is added or subtracted on the basis of the core prescription.

关键词

肾性贫血数据挖掘R语言中药用药规律

Keywords

data miningR languagetraditional Chinese medicinemedication rules

references

ZHENG L，TIAN J H，LIU D P，et al. Efficacy and safety of roxadustat for anaemia in dialysis-dependent and non-dialysis-dependent chronic kidney disease patients：a systematic review and meta-analysis[J]. Br J Clin Pharmacol，2022，88（3）：919-932.

中华医学会肾脏病学分会肾性贫血诊断和治疗共识专家组. 肾性贫血诊断与治疗中国专家共识：2018修订版[J]. 中华肾脏病杂志，2018，34（11）：860-866.

LUO J C，JENSEN D E，MARONI B J，et al. Spectrum and burden of erythropoiesis-stimulating agent hyporesponsiveness among contemporary hemodialysis patients[J]. Am J Kidney Dis，2016，68（5）：763-771.

喻影，张琳，郑笑，等. 中医药治疗肾性贫血的研究进展[J]. 中国中西医结合肾病杂志，2020，21（4）：375-376.

李堃瑛，魏日胞，李锋，等. 慢性肾脏病患者肾性贫血相关因素及中医学研究[J]. 中国中西医结合肾病杂志，2016，17（9）：770-772.

廖健. 中药治疗肾性贫血疗效和安全性的Meta分析[J]. 湖南中医杂志，2019，35（4）：127-129.

袁丽莎，魏宇，曹钋，等. 补肾健脾生血法治疗维持性血液透析肾性贫血疗效及安全性的Meta分析[J]. 世界中医药，2019，14（10）：2647-2654.

许雪莲，吴昆仑. 数据挖掘技术在中医药研究中的应用[J]. 河南中医，2020，40（11）：1633-1637.

刘钰，余卓芮，刘岱宁. R语言可视化的优势及其在空间统计教学中的应用[J]. 高教论坛，2020（5）：30-33.

国家药典委员会.中华人民共和国药典：一部[S].2020年.北京：中国医药科技出版社，2020：1-616.

南京中医药大学. 中药大辞典[M]. 2版. 上海：上海科学技术出版社，2006：1-1080.

高学敏. 中药学[M]. 2版. 北京：中国中医药出版社，2007：51-523.

樊雪鸣，王柳丁，申伟等.基于R语言数据挖掘的中药治疗紧张型头痛的用药规律探析[J].中草药，2021，52（15）：4614-4625.

王燕，周冰倩，庞龙，等.基于数据挖掘探讨中医药治疗年龄相关性黄斑变性的用药规律[J].中药新药与临床药理，2021，32（4）：579-584.

张卿，李国臣. 李国臣主任医师从肝脾论治肾性贫血[J]. 光明中医，2020，35（13）：1989-1991.

陈锦海，朱良伟，李月婷，等. 当归补血汤加味治疗慢性肾衰竭合并贫血120例[J]. 中国老年学杂志，2012，32（1）：164-165.

陈剑旭. 四君子汤联合补铁法治疗缺铁性贫血的效果观察[J/OL]. 中西医结合心血管病电子杂志，2016，4（25）：154-155[2022-10-15]. https：//kns.cnki.net/kns8/defaultresult/index.DOI：10.16282/j.cnki.cn11-9336/r.2016.25.116https://kns.cnki.net/kns8/defaultresult/index.DOI:10.16282/j.cnki.cn11-9336/r.2016.25.116.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Study on Medication Regulation and Pharmaceutical Property of Tibetan Medicine Prescription of Punica gran- atum Seeds Based on Apriori Algorithm and “Ro Nus Zhu rJes ”Vector Model

Related Author

Qieni Xiangmao

Cairangji

Wencheng Dangzhi

REN Qingcuo

JIANGYONG Silang

Zeweng Yongzhong

Nanka Jianshen

Deluo

Related Institution

No data

Address：8/F,129 Daping Main Street,Yuzhong District,Chongqing 400042,P.R.China
Technical support is provided by Beijing Founder Electronics co., LTD 渝ICP备15012710号公网安备50010302001817
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰