医学知识图谱研究与应用综述  

Survey of Medical Knowledge Graph Research and Application

作　　者：蒋川宇 韩翔宇 杨文蕊 吕博涵 黄小欧[1] 谢夏 谷阳 JIANG Chuanyu;HAN Xiangyu;YANG Wenrui;LYU Bohan;HUANG Xiaoou;XIE Xia;GU Yang(School of Computer Science and Technology,Hainan University,Haikou 570288,China)

机构地区：[1]海南大学计算机科学与技术学院,海口570228

出　　处：《计算机科学》2023年第3期83-93,共11页Computer Science

基　　金：医学大数据特殊病种诊疗模型与技术研究(ZDKJ2021042)。

摘　　要：医学数据数字化推进过程中,如何选择合适的技术来对医学数据进行高效处理和准确分析,是当今医学领域普遍面临的问题。利用具有优秀联想与推理能力的知识图谱技术来对医学数据进行处理与分析,能更好地实现智慧医疗、辅助诊断等应用。医学知识图谱的完整构建过程包括知识抽取、知识融合和知识推理。其中知识抽取可细分为实体抽取、关系抽取和属性抽取,知识融合则主要包括实体对齐和实体消歧。首先,对现今医学知识图谱的构建技术和实际应用进行归纳整理,针对每一具体构建过程阐明技术发展脉络。在此基础上,对相关技术进行介绍并说明其优点和局限性。其次,介绍几个已成熟运用的医学知识图谱。最后,根据知识图谱在医学领域的技术与应用现状,给出未来知识图谱可进行的技术兼应用性的研究方向。In the process of digitisation of medical data,choosing the right technology for efficient processing and accurate analysis of medical data is a common problem faced by the medical field today.The use of knowledge graph technology with the excellent association and reasoning capabilities to process and analyse medical data can better enable applications such as wise information technology of medicine and aided diagnoses.The complete process of constructing a medical knowledge graph includes know-ledge extraction,knowledge fusion and knowledge reasoning.Knowledge extraction can be subdivided into entity extraction,relationship extraction and attribute extraction,while knowledge fusion mainly includes entity alignment and entity disambiguation.Firstly,the construction technologies and practical applications of medical knowledge graphs are summarised,and the development of the technologies is clarified for each specific construction process.On this basis,the relevant techniques are introduced,and their advantages and limitations are explained.Secondly,introducing several medical knowledge graphs that are being successfully applied.Finally,based on the current state of technology and applications of knowledge graphs in the medical field,future research directions for knowledge graphs in technology and applications are given.

关 键 词：医学 大数据 知识图谱 数据处理 知识图谱构建技术 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]