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基于数据挖掘分析针刺改善多囊卵巢综合征高雄激素血症的用穴规律

Data mining-based analysis of acupuncture point patterns for improve hyperandrogenism in polycystic ovary syndrome
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摘要 目的 研究旨在利用数据挖掘分析针刺改善多囊卵巢综合征高雄激素血症的用穴配伍规律。方法 文献检索2020—2023年维普(VIP)、万方(Wanfang)、中国知网(CNKI)和Pub Med数据库等,创建数据库,通过频数分析、关联规则分析和聚类分析等方法进行用穴规律分析。结果 纳入文献59篇,包含71个腧穴,共使用频次553次。三阴交为使用频率最高的腧穴,按经络分任脉经穴使用频率最高。关联分析支持度最高为三阴交-足三里,聚类分析关元、三阴交和足三里的紧密度最高。结论 关元、三阴交与足三里是针刺改善多囊卵巢综合症患者高雄激素血症的主要穴位,选穴注重远近结合与祛痰湿。 Objective The study aims to utilize data mining analysis to explore the acupuncture point compatibility rules for improving hyperandrogenemia in patients with polycystic ovary syndrome(PCOS).Methods Literature search was conducted on platforms such as Wanfang,VIP,CNKI,and PubMed to construct a database from 2020 to 2024.Frequency analysis,association rule analysis,and cluster analysis were used for acupuncture point pattern analysis.Results A total of 59 articles were included,encompassing 71 acupuncture points,with a total of 553 usage frequencies.Sanyinjiao was the most frequently used acupuncture point,with the Ren Meridian acupuncture points being the most commonly used when categorized by meridian.The highest support in association analysis was found between Sanyinjiao and Zusanli,while cluster analysis showed the closest association among Guanyuan,Sanyinjiao and Zusanli.Conclusion Guanyuan,Sanyinjiao and Zusanli are the primary acupuncture points for improving hyperandrogenemia in patients with polycystic ovary syndrome,emphasizing a combination of distant and nearby points as well as phlegm-dampness elimination in point selection.
作者 郑璐瑶 缪轩磊 杨雪琴 罗佳佳 张波 Zheng Luyao;Miao Xuanlei;Yang Xueqin;Luo Jiajia;Zhang Bo(Acupunture and Tina College,Jiangxi University of Chinese Medicine,Nanchang 330004,China;the Third Department of Acupunture,the Third Affiliated Hospital of Jiangxi University of Chinese Medicine,Nanchang 330006,China)
出处 《中医外治杂志》 2025年第1期34-38,I0001,共6页 Journal of External Therapy of Traditional Chinese Medicine
基金 江西省中医药管理局项目(SZYYB20204459)。
关键词 多囊卵巢综合征 临床研究 数据挖掘 Polycystic ovary syndrome Clinical study Data mining
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