摘要
介绍中医学症状研究及中医药领域知识图谱技术应用研究现状,选取《中医药学名词》作为症状知识来源,构建中医症状知识库。运用描述性统计、关联规则分析等数据挖掘方法阐述高频证型以及症状信息分布规律、症状以及症状隐性知识之间的关联关系,利用Neo4j构建中医症状知识图谱并分析病证-症状-属性之间的知识关联关系。
The paper introduces the research status of traditional Chinese medicine(TCM)symptom and the application of knowledge graph technology in TCM field,and selects Chinese Terms in Traditional Chinese Medicine and Pharmacy as the source of symptom knowledge to build a knowledge base of TCM symptom.Descriptive statistics,association rule analysis and other data mining methods are used to elaborate the distribution rules of high-frequency syndromes and symptom information,as well as the association relationship between symptoms and symptom tacit knowledge.Neo4j is used to construct the TCM symptom knowledge graph and analyze the knowledge association relationship between syndromes,symptoms and attributes.
作者
谢文利
李君
毛树松
解丹
XIE Wenli;LI Jun;MAO Shusong;XIE Dan(College of Information Engineering,Hubei University of Chinese Medicine,Wuhan 430065,China;The First Clinical College,Hubei University of Chinese Medicine,Wuhan 430065,China;Hubei Provincial Hospital of Traditional Chinese Medicine,Wuhan 430065,China)
出处
《医学信息学杂志》
CAS
2023年第2期35-41,共7页
Journal of Medical Informatics
基金
湖北中医药大学中医药传承与创新计划“基于信息抽取技术的中医辨证传承与创新研究”(项目编号:2022SZXC012)。
关键词
中医症状
关联规则
知识图谱
Neo4j
traditional Chinese medicine(TCM)symptom
association rules
knowledge graph
Neo4j
