中医舌诊数据挖掘文献的计量与可视化分析  

Data Mining in TCM Tongue Diagnosis:the Literature of Bibliometric and Visualization Analysis

作　　者：王东军 张红日 田之魁 孙璇[2] 关媛媛 周密 朱青青 周宗慧 王泓午[2] WANG Dongjun;ZHANG Hongri;TIAN Zhikui;SUN Xuan;GUAN Yuanyuan;ZHOU Mi;ZHU Qingqing;ZHOU Zonghui;WANG Hongwu(Traditional Chinese Medical College,North China University of Science and Technology,Tangshan 063210,China;School of Public Health,Tianjin University of Traditional Chinese Medicine,Tianjin 301617,China;Rehabilitation School,Qilu Medical University,Zibo 255000,China)

机构地区：[1]华北理工大学中医学院,河北唐山063210 [2]天津中医药大学健康科学与工程学院,天津301617 [3]齐鲁医药学院康复学院,山东淄博255000

出　　处：《西部中医药》2025年第1期67-72,共6页Western Journal of Traditional Chinese Medicine

基　　金：国家重点研发计划中医药现代化研究重点专项(2017YFC1703305);河北省自然科学基金(H2023209049);河北省中医药管理局科研计划项目(2024355);华北理工大学重点研究项目(ZD-YG-202316,ZD-YG-202409)。

摘　　要：目的:对中医舌诊数据挖掘进行系统的文献计量研究与可视化分析,探究其研究历史、现状、热点及发展趋势。方法:检索中国知网(CNKI)2000年1月1日至2019年12月31日“中医舌诊数据挖掘”研究领域相关文献,并进行文献计量学分析。应用Cite Space软件对纳入文献进行作者、关键词、研究机构共现分析,并绘制相关可视化图谱。使用模块值和平均轮廓值评价可视化图谱的结构合理性,通过突现度和中介中心性辨识不同聚类中的作者、机构、关键词,对各主要关键词进行聚类分析。选择对数似然比(log-likelihood ratio,LLR)算法标记聚类,通过时间线视图展示不同聚类的相互影响以及关键词的时间跨度。结果:共获得文献461篇,发文量大的机构是北京中医药大学,发文量为57篇;中医舌诊数据挖掘文献研究领域初步形成了以北京中医药大学、上海中医药大学、南京中医药大学、天津中医药大学、山东中医药大学为代表的5个学术研究机构合作网络;上海中医药大学是中介中心性最高的机构为0.19,在机构合作网络中影响力最大;上海中医药大学、上海中医药大学附属龙华医院、天津中医药大学第二附属医院中介中心性较高(>0.1),许家佗、李刚、林凌均发文9篇,是3个最高产作者;数据挖掘、舌诊、舌象、中医证候、证候要素是中介中心性较高的高频关键词;关键词聚类分析形成了10个聚类标签。结论:中医舌诊数据挖掘文献研究数量呈稳定增长趋势,中医病证结合文本挖掘与四诊信息客观化研究、基于深度学习技术挖掘常见病证舌诊图像处理、应用人工智能大数据进行舌象处理与分析舌诊客观化、2型糖尿病流行病学特征与中医证型相关性研究、冠心病证候要素与中医客观化研究、数据挖掘名老中医临床经验及辨治规律研究等是该研究领域的热点。Objective:To survey the research history,status quo,hot spots and developmental trends of tongue diagnosis in TCM by conducting a systematic bibliometric study and visualization analysis of the literature on data mining of TCM tongue diagnosis.Methods:The relevant literature in the research field of"data mining of TCM tongue diagnosis"was searched from CNKI between January 1,2000 and December 31,2019 to perform bibliometric analysis of the literature.CiteSpace software was applied to conduct the co-occurrence analysis of the author,keywords and research institutions of the included literature,and to draw the relevant visualization maps.Modular and average contour values were used to assess the structural soundness of visualisation maps,the authors,institutions,and keywords in different clusters were identified through the prominence and betweenness centrality,and cluster analysis was performed for each major keyword.Log-likelihood ratio algorithm was chosen to label the clusters,showing the interactions of the different clusters and the time span of the keywords through a timeline view.Results:A total of 461 papers were obtained,the institution with the large number of articles was Beijing University of Chinese Medicine,with 57 articles;a cooperation network of five academic research institutions represented by Beijing University of Traditional Chinese Medicine,Shanghai University of Traditional Chinese Medicine,Nanjing University of Traditional Chinese Medicine,Tianjin University of Traditional Chinese Medicine and Shandong University of Traditional Chinese Medicine has been initially formed in the field of TCM tongue diagnosis data mining literature research;Shanghai University of Traditional Chinese Medicine is the institution with the highest betweenness centrality at 0.19,the most influential one in the institutional cooperation network;Shanghai University of Traditional Chinese Medicine,Longhua Hospital Shanghai University of TCM and Second Affiliated Hospital of Tianjin University of TCM showed higher betw

关 键 词：中医舌诊 数据挖掘 CITESPACE 可视化分析 文献计量 

分 类 号：R241.25[医药卫生—中医诊断学]