基于循证体系的针灸学数据挖掘算法构建与应用研究  

Construction and application of acupuncture data mining algorithms within anevidence-based framework

作　　者：王喆 陈芊秀 董志浩 宋庆雨 陈新勇[3] 韩晶[3] WANG Zhe;CHEN Qianxiu;DONG Zhihao;SONG Qingyu;CHEN Xinyong;HAN Jing(School of Acupuncture and Tuina,Shandong University of Traditional Chinese Medicine,Jinan 250013,P.R.China;School of Traditional Chinese Medicine,Shandong University of Traditional Chinese Medicine,Jinan 250013,P.R.China;Department of Acupuncture,Affiliated Hospital of Shandong University of Traditional Chinese Medicine,Jinan 250013,P.R.China)

机构地区：[1]山东中医药大学针灸推拿学院,济南250013 [2]山东中医药大学中医学院,济南250013 [3]山东中医药大学附属医院针灸科,济南250013

出　　处：《中国循证医学杂志》2024年第9期1070-1078,共9页Chinese Journal of Evidence-based Medicine

基　　金：国家自然科学基金面上项目(编号:82374572);山东省自然科学基金面上项目(编号:ZR2020MH365);山东省中医药科技发展计划(编号:2019-0069);泰山学者工程专项经费资助(编号:tsqn202312376)。

摘　　要：在针灸临床研究的腧穴数据挖掘工作中,文献证据质量、样本量、临床疗效等因素对结局质量的影响尚不明确,影响了数据挖掘类研究成果的证据转化与临床应用。本研究提出运用熵权和线性加权法对上述指标进行多指标决策以获得腧穴处方综合权重得分,通过加权腧穴处方进行后续数据挖掘工作的新流程。本文以偏头痛研究为例将加权算法与经典算法结果进行对比,结果显示本研究所提出的算法对选穴分散的研究更具意义,在聚类分析中能更好发现潜在的腧穴配伍规律。该算法将循证针灸学体系纳入数据挖掘工作流程,为针灸学数据挖掘相关研究质量的提升提供了新思路,但后续仍需更多的研究加以验证。In the realm of data mining based on modern acupuncture clinical research,the impact of literature features such as literature quality,evidence level,sample size,and clinical efficacy on the quality of data mining outcomes remains uncertain.These issues are significant factors restricting the translational application of data mining research results.We suggest employing both entropy weight and linear weighting techniques to assess the specified indicators.This assessment results in a comprehensive weighted score for acupuncture prescriptions,serving as the foundation for our ensuing data mining endeavors.In this study,migraine research serves as an example to contrast the efficacy of weighted algorithms against that of classical algorithms.The findings demonstrate that the algorithm introduced in this research significantly contributes to studies focusing on the dispersed selection of acupuncture points.Its superiority lies in cluster analysis,where it adeptly discerns potential patterns in the amalgamation of acupoints.This algorithm amalgamates evidence-based acupuncture with data mining processes,providing innovative perspectives that augment the caliber of research in acupuncture data mining.Nonetheless,additional research is essential to corroborate these results.

关 键 词：针灸 数据挖掘 熵权法 多指标决策 加权关联规则分析 加权聚类分析 

分 类 号：R245[医药卫生—针灸推拿学]