[1]李薇,季顺欣,冯晓玲,等.基于数据挖掘研究古代妇科医籍滑胎用药规律[J].西部中医药,2024,37(12):71-76.[doi:10.12174/j.issn.2096-9600.2024.12.17]
　LI Wei,JI Shunxin,FENG Xiaoling,et al.Study on the Medication Rule of Habitual Abortion in Ancient Gynecological Medical Books Based on Data Mining[J].Western Journal of Traditional Chinese Medicine,2024,37(12):71-76.[doi:10.12174/j.issn.2096-9600.2024.12.17]
点击复制

基于数据挖掘研究古代妇科医籍滑胎用药规律
分享到：

《西部中医药》[ISSN:2096-9600/CN:62-1204/R]

卷:
37
期数:
2024年12期
页码:
71-76
栏目:
二次研究
出版日期:
2024-12-15

文章信息/Info

Title:
Study on the Medication Rule of Habitual Abortion in Ancient Gynecological Medical Books Based on Data Mining
作者:
李薇1, 季顺欣1, 冯晓玲2, 王超男1
1.黑龙江中医药大学，黑龙江 哈尔滨 150040
2.黑龙江中医药大学附属第一医院，黑龙江 哈尔滨 150040
Author(s):
LI Wei1, JI Shunxin1, FENG Xiaoling2, WANG Chaonan1
1.Heilongjiang University of Chinese Medicine, Harbin 150040, China
2.The First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin 150040, China
关键词:
滑胎; 数据挖掘; 妇科; 用药规律
Keywords:
habitual abortion; data mining; gynecology; medication rules
分类号:
R271.41
DOI:
10.12174/j.issn.2096-9600.2024.12.17
文献标志码:
A
摘要:
目的整理古代妇科专著中关于滑胎的文献记载，探索治疗滑胎的用药特点及配伍规律，以期为当代中医临床治疗滑胎用药提供理论依据。 方法选取历代具有代表性的妇科专著，检索治疗滑胎的方剂，运用Excel进行用药频次、药物类型及性味归经的统计，运用VOSviewer 1.6.15软件分析用药的整体情况，运用Origin 2022软件绘制药物的性味归经热图，运用SPSS Modeler 18.0进行药物间的关联规则分析，运用SPSS 26.0进行高频药物的聚类分析、因子分析及决策树分析。 结果共筛选出古代妇科专著77本，纳入方剂79首，涉及中药97味，用药总频次719次。使用频次前5味的药物依次是当归、白术、川芎、甘草、人参，常用药类为补虚药、清热药、活血化瘀药等，性味以温性、甘苦味药物为主，归经以脾、肝、肾经为主。关联规则分析显示置信度最高的药对是当归-白芍、当归-川芎，聚类分析获得5个核心聚类方，因子分析提取到7个公因子，以当归为因变量的决策树模型显示白芍为最佳识别中药。 结论古代医家治疗滑胎时，注重恢复脾、肝、肾脏功能，虚则补之，如益气、养血、健脾、补肾；实则损之，如化瘀、清热，治疗总则以滋养补虚为本；灵活运用活血化瘀、清热之法，以达固冲任、安胎元之效。
Abstract:
ObjectiveTo provide theoretical evidence for clinical therapy of habitual abortion (HA) by sorting up the documentary records about habitual abortion in ancient gynecological medical books and exploring the characteristics of the medication and the laws of compatibility of the treatment of habitual abortion. MethodsRepresentative gynecological monographs of the past dynasties were chosen to search the prescriptions of treating HA, Excel was applied to conduct the statistics on the frequency of medication, types of medication and the properties, flavors and meridian tropism of the medications, VOSviewer1.6.15 software was used to analyze the general conditions of the medications, Origin 2022 software was utilized to draw the thermal map of the nature, flavor and meridian tropism, SPSS Modeler 18.0 to analyze association rules between the drugs, SPSS 26.0 to perform cluster analysis, factor analysis and decision tree of high-frequency drugs. ResultsA total of 77 ancient gynaecological monographs were screened, and 79 prescriptions were included, involving 97 Chinese medicines were involved, and the total frequency of medication was 719 times. The drugs used in the top five frequencies were Danggui (Angelicae sinensis radix), Baizhu (Atractylodes macrocephala Koidz.), Chuanxiong(Chuanxiong rhizoma), Gancao(Glycyrrhizae radix et rhizoma) and ginseng, the commonly used drugs were these of tonification, clearing heat, activating blood and resolving stagnation, belonging to the warm nature, sweet and bitter flavors, mainly entering the meridians of spleen, liver and kidney. Association rule analysis showed that the drug combinations with the highest confidence level were Danggui-Baishao, Danggui-Chuanxiong. Cluster analysis has yielded five core clustering formulae, factor analysis has extracted seven common factors, the decision tree model with Danggui as the dependent variable showed that Baishao was the best identified. ConclusionIn clinical treatment of HA, ancient physicians paid attention to the functional restoration of spleen, liver and kidney, benefitting the deficiency, including invigorating Qi, nourishing blood, invigorating spleen and benefitting kidney; reducing the excess, covering resolving the stasis and clearing heat, they adopted nourishing and replenishing the deficiency as therapeutic principles; flexible application of the methods of activating blood and resolving stagnation, clearing heat could obtain the effects of strengthening Chong-Ren and preserving the natal element.

相似文献/References:

[1]王贵民,李廷保△.中医药治疗前列腺疾病用药规律的数据挖掘研究[J].西部中医药,2014,27(05):77.
　WANG Guimin,LI Tingbao.Data Mining Research on Medication Rules of TCM Treatment for Prostatic Diseases[J].Western Journal of Traditional Chinese Medicine,2014,27(12):77.
[2]马晓霞,李廷保△.基于数据挖掘治疗糖尿病的中药配伍体系研究[J].西部中医药,2015,28(03):100.
[3]刘莉莉.基于数据仓库的医院临床决策支持系统研究[J].西部中医药,2015,28(06):52.[doi:2015/6/16 0:00:00]
[4]潘 文.基于数据挖掘的治疗原发性痛经方证与核心药物配伍规律分析[J].西部中医药,2015,28(12):75.
[5]舒劲,武正权,王煜,等.名老中医王自立运脾系列方剂方证知识数据挖掘研究*[J].西部中医药,2013,26(02):45.
　SHU Jin,WU Zhengquan,WANG Yu,et al.Knowledge Mining of Prescriptions and Syndromes in Famous Veteran TCM Doctor WANG Zili's YunPi Prescriptions Series Based on Data Mining Method[J].Western Journal of Traditional Chinese Medicine,2013,26(12):45.
[6]滕璐灵,宋尧,张宝林.基于Apriori改进算法的中药处方分析技术[J].西部中医药,2014,27(05):48.
　TENG Luling,SONG Yao,ZHANG Baolin.The Technique of TCM Prescription Analysis Based on Apriori Improved Algorithm[J].Western Journal of Traditional Chinese Medicine,2014,27(12):48.
[7]武正权,舒劲,王煜,等.名老中医王自立“清利通淋汤”方证知识数据挖掘研究[J].西部中医药,2017,30(01):49.
　WU Zhengquan,SHU Jin,WANG Yu,et al.Study on Data Mining of Syndrome Knowledge of Famous Veteran TCM Doctors WANG Zili′s QingLi TongLin Tang[J].Western Journal of Traditional Chinese Medicine,2017,30(12):49.
[8]陈俐禄.数据挖掘中药复方治疗痛经的用药规律[J].西部中医药,2016,29(04):86.
　CHEN Lilu.Medication Rules of Herbal Compounds in Treating Dysmenorrhea Based on Data Mining[J].Western Journal of Traditional Chinese Medicine,2016,29(12):86.
[9]宋陈惠,王海瑜,诸葛叶婷,等.针刺治疗干燥综合征选穴规律的文献分析[J].西部中医药,2018,31(10):40.
　SONG Chenhui,WANG Haiyu,ZHUGE Yeting,et al.Analysis of the Literature about the Rule of Acupoints Selection for Acupuncture in Treating Sjogren′s Syndrome[J].Western Journal of Traditional Chinese Medicine,2018,31(12):40.
[10]王瑞祥.抛锚式教学在中医院校数据挖掘实验教学中的应用[J].西部中医药,2017,30(04):54.
　WANG Ruixiang.Application of Anchored Instruction in the Data Mining Experiment Teaching of TCM Colleges[J].Western Journal of Traditional Chinese Medicine,2017,30(12):54.

备注/Memo

备注/Memo:
李薇（2000—），女，在读硕士研究生。研究方向：中医临床常见病症的诊疗理论和方药规律研究。黑龙江省自然科学基金（ZD2021H006）；黑龙江省中医药经典普及化专项课题（ZYW2024-063）。
更新日期/Last Update: 2024-12-15