中文医学知识图谱研究及应用进展

doi:10.3778/j.issn.1673-9418.2112118

计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (10): 2219-2233.DOI: 10.3778/j.issn.1673-9418.2112118

中文医学知识图谱研究及应用进展

范媛媛, 李忠民⁺()

中南大学生命科学学院,长沙 410013

收稿日期:2021-12-29 修回日期:2022-05-13 出版日期:2022-10-01 发布日期:2022-10-14
通讯作者: + E-mail: tmbs300600@163.com
作者简介:范媛媛（1997—）,女,河南孟州人,硕士研究生,主要研究方向为信息组织、知识图谱。
李忠民（1971—）,女,湖南邵阳人,博士,副教授,硕士生导师,主要研究方向为医学信息学、医学信息组织。
基金资助:
中南大学中央高校基本科研业务费专项资金(2021zzts0558);湖南省研究生科研创新项目(CX20210122)

Research and Application Progress of Chinese Medical Knowledge Graph

FAN Yuanyuan, LI Zhongmin⁺()

College of Life Science, Central South University, Changsha 410013, China

Received:2021-12-29 Revised:2022-05-13 Online:2022-10-01 Published:2022-10-14
About author:FAN Yuanyuan, born in 1997, M.S. candidate. Her research interests include organization of information and knowledge graph.
LI Zhongmin, born in 1971, Ph.D., associate professor, M.S. supervisor. Her research interests include medical informatics and medical information organization.
Supported by:
Fundamental Research Funds for the Central Universities of Central South University(2021zzts0558);Postgraduate Scientific Research Innovation Project of Hunan Province(CX20210122)

摘要/Abstract

摘要：

知识图谱是赋予机器背景知识的大规模语义网络。利用知识图谱对多源异构的医学信息进行有序化组织,能有效提升海量医学资源的利用价值,推动医学智能化发展。从知识图谱的关键技术、医学知识图谱构建以及医学知识图谱的应用三个维度刻画医学领域知识图谱研究、构建与应用现状,探索未来值得研究的课题。首先,系统梳理知识表示、知识抽取、知识融合以及知识推理四种知识图谱构建关键技术的发展脉络并探讨其研究进展,分析中文医学知识图谱构建的技术难点;其次,从医学本体、全科医学知识图谱和单病种医学知识图谱三个角度阐述中文医学知识图谱已有研究并分析了中文医学知识图谱的研究特点;最后,对中文医学知识图谱在语义搜索、决策支持以及智能问答等方面的应用研究进行分析并探讨新的应用场景。针对中文医学知识图谱研究面临的术语标准化程度不高、标注语料缺乏、技术研究不够深入以及应用场景有局限性等挑战,对其未来的研究方向做出了展望。

关键词: 医学知识图谱, 知识表示, 知识抽取, 决策支持, 智能问答

Abstract:

Knowledge graph is a large-scale semantic network that gives machine background knowledge. Using knowledge graph to organize heterogeneous medical information can effectively improve the utilization value of massive medical resources and promote the development of medical intelligence. This paper describes the research, construction and application status of knowledge graph in medical field from three dimensions: the key technology of knowledge graph, the construction of medical knowledge graph and the application of medical knowledge graph, and explores the topics worthy of research in the future. Firstly, the development of knowledge representation, knowledge extraction, knowledge fusion and knowledge inference are systematically summarized, their latest progress is discussed, and the technical difficulties in the construction of Chinese medical knowledge graph are analyzed. Secondly, the existing research on Chinese medical knowledge graph is illustrated from three perspectives of medical ontology, general practice knowledge graph and single disease medical knowledge graph. The research characteristics of Chinese medical knowledge graph are also analyzed. Finally, the application of medical know-ledge graph in semantic search, decision support and intelligent question answering are analyzed, and the new app-lication scenarios are discussed. In view of the challenges faced by Chinese medical knowledge graph, such as low standardization of terminology, lack of annotated corpus, insufficient technical research and limitations of applica-tion scenarios, the future research directions of Chinese medical knowledge graph are prospected.

Key words: medical knowledge graph, knowledge representation, knowledge extraction, decision support, intelli-gent question answering

中图分类号:

TP18

范媛媛, 李忠民. 中文医学知识图谱研究及应用进展[J]. 计算机科学与探索, 2022, 16(10): 2219-2233.

FAN Yuanyuan, LI Zhongmin. Research and Application Progress of Chinese Medical Knowledge Graph[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(10): 2219-2233.

图/表 2

参考文献 132

[1]	SINGHAL A. Introducing the knowledge graph: things, not strings[J]. Official Google Blog, 2012, 5: 16.
[2]	刘峤, 李杨, 段宏, 等. 知识图谱构建技术综述[J]. 计算机研究与发展, 2016, 53(3): 582-600.
	LIU Q, LI Y, DUAN H, et al. Knowledge graph construction technology[J]. Journal of Computer Research and Develop-ment, 2016, 53(3): 582-600.
[3]	LEHMANN J, ISELE R, JAKOB M, et al. DBpedia: a large-scale, multilingual knowledge base extracted from wikipedia[J]. Semantic Web, 2015, 6(2): 167-195. DOI URL
[4]	BOLLACKER K D, COOK R P, TUFTS P. Freebase: a shared database of structured general human knowledge[C]// Procee-dings of the 22nd AAAI Conference on Artificial Intelligence, Vancouver, Jul 22-26, 2007. Menlo Park: AAAI, 2007: 1962-1963.
[5]	SUCHANEK F M, KASNECI G, WEIKUM G. Yago: a large ontology from Wikipedia and WordNet[J]. Journal of Web Semantics, 2008, 6(3): 203-217. DOI URL
[6]	XU B, LIANG J, XIE C, et al. CN-DBpedia2: an extraction and verification framework for enriching Chinese encyclopedia knowledge base[J]. Data Intelligence, 2019, 1(3): 271-288. DOI URL
[7]	NIU X, SUN X R, WANG H F, et al. Zhishi.me-weaving Chinese linking open data[C]// LNCS 7032: Proceedings of the 10th International Semantic Web Conference, Bonn, Oct 23-27, 2011. Berlin, Heidelberg: Springer, 2011: 205-220.
[8]	CHEN H J, HU N, QI G L, et al. OpenKG chain: a blockchain infrastructure for open knowledge graphs[J]. Data Intelli-gence, 2021, 3(2): 205-227.
[9]	MCDONALD F S, ELKIN P L. UMLS concept indexing for production databases: a feasibility study[J]. Journal of the American Medical Informatics Association, 2001, 8(5): 512-514. PMID
[10]	DONNELLY K. SNOMED C T. The advanced terminology and coding system for eHealth[J]. Studies in Health Techno-logy and Informatics, 2006, 121: 279-290.
[11]	李丹亚, 胡铁军, 李军莲, 等. 中文一体化医学语言系统的构建与应用[J]. 情报杂志, 2011, 30(2): 147-151.
	LI Y D, HU T J, LI J L, et al. Construction and application of Chinese unified medical language system[J]. Journal of Intelligence, 2011, 30(2): 147-151.
[12]	贾李蓉, 刘静, 于彤, 等. 中医药知识图谱构建[J]. 医学信息学杂志, 2015, 36(8): 51-53.
	JIA L R, LIU J, YU T, et al. Construction of traditional Chinese medicine knowledge graph[J]. Journal of Medical Informatics, 2015, 36(8): 51-53.
[13]	NICHOLSON D N, GREENE C S. Constructing knowledge graphs and their biomedical applications[J]. Computational and Structural Biotechnology Journal, 2020, 18: 1414-1428. DOI PMID
[14]	MOHAMED S K, NOUNU A, NOVÁČEK V. Biological applications of knowledge graph embedding models[J]. Brie-fings in Bioinformatics, 2021, 22(2): 1679-1693.
[15]	MACLEAN F. Knowledge graphs and their applications in drug discovery[J]. Expert Opinion on Drug Discovery, 2021, 16(9): 1057-1069. DOI URL
[16]	侯梦薇, 卫荣, 陆亮, 等. 知识图谱研究综述及其在医疗领域的应用[J]. 计算机研究与发展, 2018, 55(12): 2587-2599.
	HOU M W, WEI R, LU L, et al. Research review of know-ledge graph and its application in medical domain[J]. Journal of Computer Research and Development, 2018, 55(12): 2587-2599.
[17]	修晓蕾, 吴思竹, 崔佳伟, 等. 医学知识图谱构建研究进展[J]. 中华医学图书情报杂志, 2018, 27(10): 33-39.
	XIU X L, WU S Z, CUI J W, et al. Advances in studies on construction of medical knowledge graphs[J]. Chinese Journal of Medical Library and Information Science, 2018, 27(10): 33-39.
[18]	袁凯琦, 邓扬, 陈道源, 等. 医学知识图谱构建技术与研究进展[J]. 计算机应用研究, 2018, 35(7): 1929-1936.
	YUAN K Q, DENG Y, CHEN D Y, et al. Construction techniques and research development of medical knowledge graph[J]. Application Research of Computers, 2018, 35(7): 1929-1936.
[19]	刘烨宸, 李华昱. 领域知识图谱研究综述[J]. 计算机系统应用, 2020, 29(6): 1-12.
	LIU Y C, LI H Y. Survey on domain knowledge graph res-earch[J]. Computer Systems & Applications, 2020, 29(6): 1-12.
[20]	谭玲, 鄂海红, 匡泽民, 等. 医学知识图谱构建关键技术及研究进展[J]. 大数据, 2021, 7(4): 80-104.
	TAN L, E H H, KUANG Z M, et al. Key technologies and research progress of medical knowledge graph construction[J]. Big Data Research, 2021, 7(4): 80-104.
[21]	赵悦淑, 王军, 王蕊, 等. 中文医学知识图谱研究进展[J]. 中国数字医学, 2021, 16(6): 86-91.
	ZHAO Y S, WANG J, WANG R, et al. Advances in studies on Chinese medical knowledge graph[J]. China Digital Me-dicine, 2021, 16(6): 86-91.
[22]	SOOMRO P D, KUMAR S, BANBHRANI A A S, et al. Bio-NER: biomedical named entity recognition using rule-based and statistical learners[J]. International Journal of Ad-vanced Computer Science and Applications, 2017, 8(12): 163-170.
[23]	LAKEL K, BENDELLA F, BENKHADDA S. Named entity recognition for psychological domain: challenges in document annotation for the Arabic language[C]// Proceedings of the 1st International Conference on Embedded & Distributed Systems, Oran, Dec 17-18, 2017. Piscataway: IEEE, 2017: 39-43.
[24]	WEEGAR R, PÉREZ A, CASILLAS A, et al. Deep medical entity recognition for Swedish and Spanish[C]// Proceedings of the 2018 IEEE International Conference on Bioinformatics and Biomedicine, Madrid, Dec 3-6, 2018. Piscataway: IEEE, 2018: 1595-1601.
[25]	WEEGAR R, PÉREZ A, CASILLAS A, et al. Recent advances in Swedish and Spanish medical entity recognition in clinical texts using deep neural approaches[J]. BMC Medical Infor-matics and Decision Making, 2019, 19(7): 1-14.
[26]	王昊奋, 漆桂林, 陈华钧. 知识图谱方法、实践与应用[M]. 北京: 电子工业出版社, 2019.
	WANG H F, QI G L, CHEN H J. Knowledge graph method, practice and application[M]. Beijing: Publishing House of Electronics Industry, 2019.
[27]	肖仰华, 徐波, 林欣, 等. 知识图谱概念与技术[M]. 北京: 电子工业出版社, 2020.
	XIAO Y H, XU B, LIN X, et al. Concept and technology of knowledge graph[M]. Beijing: Publishing House of Electronics Industry, 2020.
[28]	SHORTLIFFE E H. Computer-based medical consultations: MYCIN[M]. New York: Elsevier Scientific Publishing Com-pany, Inc., 1976.
[29]	MINSKY M. A framework for representing knowledge[J]. Computation & Intelligence, 1995: 163-189.
[30]	COLLINS A M, QUILLIAN M R. Retrieval time from se-mantic memory[J]. Journal of Verbal Learning and Verbal Behavior, 1969, 8(2): 240-247. DOI URL
[31]	BAADER F, HORROCKS I, SATTLER U. Description logics[J]. Foundations of Artificial Intelligence, 2008, 3: 135-179.
[32]	刘知远, 孙茂松, 林衍凯, 等. 知识表示学习研究进展[J]. 计算机研究与发展, 2016, 53(2): 247-261.
	LIU Z Y, SUN M S, LIN Y K, et al. Knowledge represen-tation learning: a review[J]. Journal of Computer Research and Development, 2016, 53(2): 247-261.
[33]	MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositio-nality[C]// Proceedings of the 27th Annual Conference on Neural Information Processing Systems, Lake Tahoe, Dec 5-8, 2013. Red Hook: Curran Associates, 2013: 3111-3119.
[34]	BORDES A, USUNIER N, GARCÍA-DURÁN A, et al. Translating embeddings for modeling multi-relational data[C]// Proceedings of the 27th Annual Conference on Neural Information Processing Systems, Lake Tahoe, Dec 5-8, 2013. Red Hook: Curran Associates, 2013: 2787-2795.
[35]	WANG Z, ZHANG J W, FENG J L, et al. Knowledge graph embedding by translating on hyperplanes[C]// Proceedings of the 28th AAAI Conference on Artificial Intelligence, Québec City, Jul 27-31, 2014. Menlo Park: AAAI, 2014: 1112-1119.
[36]	JI G L, HE S Z, XU L H, et al. Knowledge graph embed-ding via dynamic graphping matrix[C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Na-tural Language Processing, Beijing, Jul 26-31, 2015. Strouds-burg: ACL, 2015: 687-696.
[37]	LIN Y, LIU Z, SUN M, et al. Learning entity and relation embeddings for knowledge graph completion[C]// Proceedings of the 29th AAAI Conference on Artificial Intelligence, Aus-tin, Jan 25-30, 2015. Menlo Park: AAAI, 2015: 2181-2187.
[38]	XIAO H, HUANG M L, ZHUX Y. TransG: a generative model for knowledge graph embedding[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Lin-guistics, Berlin, Aug 7-12, 2016. Stroudsburg: ACL, 2016: 2316-2325.
[39]	CHOI W, LEE H. Inference of biomedical relations among chemicals, genes, diseases, and symptoms using knowledge representation learning[J]. IEEE Access, 2019, 7: 179373-179384. DOI URL
[40]	CHANG D, BALAŽEVIĆ I, ALLEN C, et al. Benchmark and best practices for biomedical knowledge graph embed-dings[C]// Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing. Stroudsburg: ACL, 2020: 167-176.
[41]	PEROZZI B, AL-RFOU R, SKIENA S. DeepWalk: online learning of social representations[C]// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining, New York, Aug 24-27, 2014. New York: ACM, 2014: 701-710.
[42]	GROVER A, LESKOVEC J. Node2Vec:scalable feature lear-ning for networks[C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, Aug 13-17, 2016. New York: ACM, 2016: 855-864.
[43]	WANG D X, CUI P, ZHU W W. Structural deep network embedding[C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, Aug 13-17, 2016. New York: ACM, 2016: 1225-1234.
[44]	ZHAO C, JIANG J, GUAN Y, et al. EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning[J]. Artificial Intelligence in Medicine, 2018, 87: 49-59. DOI PMID
[45]	LI L F, WANG P, WANG Y, et al. PrTransH: embedding probabilistic medical knowledge from real world EMR data[J]. arXiv:1909.00672, 2019.
[46]	LI L, WANG P, YAN J, et al. Real-world data medical knowledge graph: construction and applications[J]. Artificial Intelligence in Medicine, 2020, 103(19): 101817. DOI URL
[47]	沈思, 孙豪, 王东波. 基于深度学习表示的医学主题语义相似度计算及知识发现研究[J]. 情报理论与实践, 2020, 43(5): 183-190.
	SHEN S, SUN H, WANG D B. Research on topics semantic similarity calculation and knowledge discovery of medical based on deep learning representation[J]. Information Studies: Theory & Application, 2020, 43(5): 183-190.
[48]	隋明爽, 崔雷. 结合多种特征的CRF模型用于化学物质-疾病命名实体识别[J]. 现代图书情报技术, 2016(10): 91-97.
	SUI M S, CUI L. Extracting chemical and disease named entities with multiple-feature CRF model[J]. New Technology of Library and Information Service, 2016(10): 91-97.
[49]	李丽双, 何红磊, 刘珊珊, 等. 基于词表示方法的生物医学命名实体识别[J]. 小型微型计算机系统, 2016, 37(2): 302-307.
	LI L S, HE H L, LIU S S, et al. Research of word rep-resentations on biomedical named entities recognition[J]. Journal of Chinese Computer Systems, 2016, 37(2): 302-307.
[50]	栗伟, 赵大哲, 李博, 等. CRF与规则相结合的医学病历实体识别[J]. 计算机应用研究, 2015, 32(4): 1082-1086.
	LI W, ZHAO D Z, LI B, et al. Combining CRF and rule based medical named entity recognition[J]. Application Res-earch of Computers, 2015, 32(4): 1082-1086.
[51]	LAMPLE G, BALLESTEROS M, SUBRAMANIAN S, et al. Neural architectures for named entity recognition[J]. arXiv: 1603.01360, 2016.
[52]	李丽双, 郭元凯. 基于CNN-BLSTM-CRF模型的生物医学命名实体识别[J]. 中文信息学报, 2018, 32(1): 116-122.
	LI L S, GUO Y K. Biomedical named entity recognition with CNN-BLSTM-CRF[J]. Journal of Chinese Information Processing, 2018, 32(1): 116-122.
[53]	杨培, 杨志豪, 罗凌, 等. 基于注意机制的化学药物命名实体识别[J]. 计算机研究与发展, 2018, 55(7): 1548-1556.
	YANG P, YANG Z H, LUO L, et al. An attention-based approach for chemical compound and drug named entitiy recognition[J]. Journal of Computer Research and Develop-ment, 2018, 55(7): 1548-1556.
[54]	LI L, JIANG Y. Integrating language model and reading control gate in BLSTM-CRF for biomedical named entity recognition[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2018, 17(3): 841-846. DOI URL
[55]	GAO W C, ZHENG X H, ZHAO S S. Named entity recog-nition method of Chinese EMR based on BERT-BiLSTM-CRF[C]// Proceedings of the 4th International Conference on Advanced Algorithms and Control Engineering, Sanya, Jan 29-31, 2021. Bristol: IOP Publishing, 2021: 012083.
[56]	RAMACHANDRAN R, ARUTCHELVAN K. Named entity recognition on bio-medical literature documents using hybrid based approach[J]. Journal of Ambient Intelligence and Hu-manized Computing, 2021. DOI: 10.1007/s12652-021-03078-z. DOI
[57]	KAMBHATLA N. Combining lexical, syntactic, and semantic features with maximum entropy models for information extraction[C]// Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, Barcelona, Jul 21-26, 2004. Stroudsburg: ACL, 2004: 178-181.
[58]	刘克彬, 李芳, 刘磊, 等. 基于核函数中文关系自动抽取系统的实现[J]. 计算机研究与发展, 2007, 44(8): 1406-1411.
	LIU K B, LI F, LIU L, et al. Implementation of a kernel-based Chinese relation extraction system[J]. Journal of Com-puter Research and Development, 2007, 44(8): 1406-1411.
[59]	UZUNER O, MAILOA J, RYAN R, et al. Semantic relations for problem-oriented medical records[J]. Artificial Intelligence in Medicine, 2010, 50(2): 63-73. DOI PMID
[60]	JI G, LIU K, HE S, et al. Distant supervision for relation extraction with sentence-level attention and entity descrip-tions[C]// Proceedings of the 31st AAAI Conference on Arti-ficial Intelligence, San Francisco, Feb 4-9, 2017. Menlo Park: AAAI, 2017: 3060-3066.
[61]	FENG J, HUANG M, ZHAO L, et al. Reinforcement learning for relation classification from noisy data[C]// Proceedings of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, Feb 2-7, 2018. Menlo Park: AAAI, 2018: 5779-5786.
[62]	CARLSON A, BETTERIDGE J, WANG R C, et al. Coupled semi-supervised learning for information extraction[C]// Pro-ceedings of the 3rd International Conference on Web Search and Web Data Mining, New York, Feb 4-6, 2010. New York: ACM, 2010: 101-110.
[63]	曹春萍, 何亚喆. 融合BSRU和ATT-CNN的化学物质与疾病的关系抽取方法[J]. 小型微型计算机系统, 2020, 41(4): 794-799.
	CAO C P, HE Y Z. Extracting relationships between che-mical substances and diseases with bidirectional simple re-current unit and attention based convolutional neural net-work[J]. Journal of Chinese Computer Systems, 2020, 41(4): 794-799.
[64]	丁泽源, 杨志豪, 罗凌, 等. 基于深度学习的中文生物医学实体关系抽取系统[J]. 中文信息学报, 2021, 35(5): 70-76.
	DING Z Y, YANG Z H, LUO L, et al. A Chinese bio-medical entity relationship extraction system based on deep learning[J]. Journal of Chinese Information Processing, 2021, 35(5): 70-76.
[65]	高峰, 杨佳欣, 顾进广. 融合关系发现词与深度学习的诊疗关系抽取[J]. 计算机应用与软件, 2021, 38(12): 168-173.
	GAO F, YANG J X, GU J G. Extraction of diagnosis and treatment relationship based on fusion relation discovery words and deep learning[J]. Computer Applications and Soft-ware, 2021, 38(12): 168-173.
[66]	武小平, 张强, 赵芳, 等. 基于BERT的心血管医疗指南实体关系抽取方法[J]. 计算机应用, 2021, 41(1): 145-149. DOI
	WU X P, ZHANG Q, ZHAO F, et al. Entity relation ex-traction method for guidelines of cardiovascular disease based on bidirectional encoder representation from transfor-mers[J]. Journal of Computer Applications, 2021, 41(1): 145-149.
[67]	MCCARTHY J F, LEHNERT W G. Using decision trees for coreference resolution[C]// Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montréal Québec, Aug 20-25, 1995. San Mateo: Morgan Kaufmann, 1995: 1050-1055.
[68]	苏佳林, 王元卓, 靳小龙, 等. 自适应属性选择的实体对齐方法[J]. 山东大学学报(工学版), 2020, 50(1): 14-20.
	SU J L, WANG Y Z, JIN X L, et al. Entity alignment method based on adaptive attribute selection[J]. Journal of Shandong University (Engineering Science), 2020, 50(1): 14-20.
[69]	李文娜, 张智雄. 基于联合语义表示的不同知识库中的实体对齐方法研究[J]. 数据分析与知识发现, 2021, 5(7): 1-9.
	LI W N, ZHANG Z X. Entity alignment method for dif-ferent knowledge repositories with joint semantic represen-tation[J]. Data Analysis and Knowledge Discovery, 2021, 5(7): 1-9.
[70]	ZHANG J, ZHANG Z, ZHANG H, et al. From electronic health records to terminology base: a novel knowledge base enrichment approach[J]. Journal of Biomedical Informatics, 2021, 113: 103628. DOI URL
[71]	BAGGA A, BALDWIN B. Entity-based cross-document core-ferencing using the vector space model[C]// Proceedings of the 36th Annual Meeting of the Association for Computa-tional Linguistics and 17th International Conference on Com-putational Linguistics, Quebec, Aug 10-14, 1998. Strouds-burg: ACL, 1998: 79-85.
[72]	ZHU G, IGLESIAS C A. Exploiting semantic similarity for named entity disambiguation in knowledge graphs[J]. Expert Systems with Applications, 2018, 101: 8-24. DOI URL
[73]	HAN X P, ZHAO J. Named entity disambiguation by leve-raging Wikipedia semantic knowledge[C]// Proceedings of the 18th ACM Conference on Information and Knowledge Management, Hong Kong, China, Nov 2-6, 2009. New York: ACM, 2009: 215-224.
[74]	王静, 谭绍峰, 贺东东, 等. 基于上下文特征的领域文献实体消歧算法[J]. 北京生物医学工程, 2018, 37(4): 398-402.
	WANG J, TAN S F, HE D D, et al. Entity disambiguation algorithm for domain document based on context feature[J]. Beijing Biomedical Engineering, 2018, 37(4): 398-402.
[75]	DUQUE A, STEVENSON M, MARTINEZ-ROMO J, et al. Co-occurrence graphs for word sense disambiguation in the biomedical domain[J]. Artificial Intelligence in Medicine, 2018, 87: 9-19. DOI PMID
[76]	VRETINARIS A, LEI C, EFTHYMIOU V, et al. Medical entity disambiguation using graph neural networks[C]// Proceedings of the 2021 International Conference on Ma-nagement of Data. New York: ACM, 2021: 2310-2318.
[77]	BOUSQUET C, HENEGAR C, LOUËT A L, et al. Imple-mentation of automated signal generation in pharmacovi-gilance using a knowledge-based approach[J]. International Journal of Medical Informatics, 2005, 74(7/8): 563-571. DOI URL
[78]	CHEN R C, HUANG Y H, BAU C T, et al. A recommenda-tion system based on domain ontology and SWRL for anti-diabetic drugs selection[J]. Expert Systems with Applications, 2012, 39(4): 3995-4006. DOI URL
[79]	沈亚诚, 舒忠梅. 基于案例推理的病历表示与系统架构研究[J]. 南方医科大学学报, 2007, 27(7): 1114-1116.
	SHEN Y C, SHU Z M. Study on medical record repre-sentation and system architecture based on case reasoning[J]. Journal of Southern Medical University, 2007, 27(7): 1114-1116.
[80]	PING X O, TSENG Y J, LIN Y P, et al. A multiple measure-ments case-based reasoning method for predicting recurrent status of liver cancer patients[J]. Computers in Industry, 2015, 69: 12-21. DOI URL
[81]	陈延雪, 杨长春, 葛天一, 等. 基于知识推理的医疗应急响应机制研究[J]. 小型微型计算机系统, 2022, 43(3): 638-643.
	CHEN Y X, YANG C C, GE T Y, et al. Research on me-dical emergency response mechanism based on knowledge reasoning[J]. Journal of Chinese Computer Systems, 2022, 43(3): 638-643.
[82]	IOANNIDIS V N, MARQUES A G, GIANNAKIS G B. Graph neural networks for predicting protein functions[C]// Proceedings of the 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Proces-sing, Le Gosier, Dec 15-18, 2019. Piscataway: IEEE, 2019: 221-225.
[83]	WANG S, REN P, CHEN Z, et al. Order-free medicine com-bination prediction with graph convolutional reinforcement learning[C]// Proceedings of the 28th ACM International Con-ference on Information and Knowledge Management, Beijing, Nov 3-7, 2019. New York: ACM, 2019: 1623-1632.
[84]	WOENSEL W V A N, ARMSTRONG C, RAJARATNAM M, et al. Using knowledge graphs to plausibly infer missing associations in EMR data[J]. Public Health and Informatics: Proceedings of MIE, 2021, 281: 417-421.
[85]	陈德华, 殷苏娜, 乐嘉锦, 等. 一种面向临床领域时序知识图谱的链接预测模型[J]. 计算机研究与发展, 2017, 54(12): 2687-2697.
	CHEN D H, YIN S N, YUE J J, et al. A link prediction model for clinical temporal knowledge graph[J]. Journal of Computer Research and Development, 2017, 54(12): 2687-2697.
[86]	GONG F, WANG M, WANG H, et al. SMR: medical know-ledge graph embedding for safe medicine recommendation[J]. Big Data Research, 2021, 23: 100174. DOI URL
[87]	李健康, 张春辉. 本体研究及其应用进展[J]. 图书馆论坛, 2004, 24(6): 80-86.
	LI J K, ZHANG C H. Research and application develop-ment on ontology[J]. Library Tribune, 2004, 24(6): 80-86.
[88]	SHEPHERD M, SAMPALLI T. Ontology as boundary object[C]// Proceedings of the 12th International ISKO Conference, Mysore, Aug 6-9, 2012. Würzburg: Ergon, 2012: 131-137.
[89]	牟冬梅, 张艳侠, 黄丽丽, 等. 基于SNOMED CT和FCA的医学领域本体构建研究[J]. 情报学报, 2013, 32(6): 653-662.
	MU D M, ZHANG Y X, HUANG L L, et al. Constructing medical ontology based on SNOMED CT and FCA[J]. Journal of the China Society for Scientific and Technical Information, 2013, 32(6): 653-662.
[90]	李晓瑛, 李丹亚, 夏光辉, 等. 肿瘤本体构建研究[J]. 数字图书馆论坛, 2015(8): 37-42.
	LI X Y, LI D Y, XIA G H, et al. Research on the cons-truction of tumor ontology[J]. Digital Library Forum, 2015(8): 37-42.
[91]	任慧玲, 李晓瑛, 王哲, 等. 面向分类统计的传统医学疾病本体构建研究[J]. 中华医学图书情报杂志, 2020, 29(11): 1-7.
	REN H L, LI X Y, WNAG Z, et al. Classification and statistics-oriented development of disease ontology for tra-ditional Chinese medicine[J]. Chinese Journal of Medical Library and Information Science, 2020, 29(11): 1-7.
[92]	HOU L, WU M, KANG H Y, et al. PMO: a knowledge representation model towards precision medicine[J]. Mathe-matical Biosciences and Engineering, 2020, 17(4): 4098-4114.
[93]	LIN K H, WU M S, WANG X L, et al. MEDLedge: a Q&A based system for constructing medical knowledge base[C]// Proceedings of the 11th International Conference on Com-puter Science & Education, Nagoya, Aug 23-25, 2016. Pis-cataway: IEEE, 2016: 485-489.
[94]	刘燕, 傅智杰, 李姣, 等. 医学百科知识图谱构建[J]. 中华医学图书情报杂志, 2018, 27(6): 28-34.
	LIU Y, FU Z J, LI J, et al. Generation of medical encyc-lopedia knowledge graph[J]. Chinese Journal of Medical Library and Information Science, 2018, 27(6): 28-34.
[95]	魏自强, 郑伟伟, 许永康. 基于百科知识的医疗数据知识图谱构建[J]. 网络安全技术与应用, 2020(10): 86-88.
	WEI Z Q, ZHENG W W, XU Y K. Construction of a medical data knowledge graph based on the encyclopedia knowledge[J]. Network Security Technology & Application, 2020(10): 86-88.
[96]	SHI L, LI S, YANG X, et al. Semantic health knowledge graph: semantic integration of heterogeneous medical knowledge and services[J]. BioMed Research International, 2017: 2858423.
[97]	黄梦醒, 李梦龙, 韩惠蕊. 基于电子病历的实体识别和知识图谱构建的研究[J]. 计算机应用研究, 2019, 36(12): 3735-3739.
	HUANG M X, LI M L, HAN H R. Research on entity recognition and knowledge graph construction based on elec-tronic medical records[J]. Application Research of Computers, 2019, 36(12): 3735-3739.
[98]	谢沂林, 蔡培强, 姜伟, 等. 基于图数据库的电子病历存储方法[J]. 信息技术与信息化, 2021(8): 134-137.
	XIE Y L, CAI P Q, JIANG W, et al. Electronic medical record storage method based on the graph database[J]. In-formation Technology and Informatization, 2021(8): 134-137.
[99]	聂莉莉, 李传富, 许晓倩, 等. 人工智能在医学诊断知识图谱构建中的应用研究[J]. 医学信息学杂志, 2018, 39(6): 7-12.
	NIE L L, LI C F, XU X Q, et al. Study on application of artificial intelligence in the building of medical diagnosis knowledge graph[J]. Journal of Medical Informatics, 2018, 39(6): 7-12.
[100]	阮彤, 孙程琳, 王昊奋, 等. 中医药知识图谱构建与应用[J]. 医学信息学杂志, 2016, 37(4): 8-13.
	RUAN T, SUN C L, WANG H F, et al. Construction of tra-ditional Chinese medicine knowledge graph and its appli-cation[J]. Journal of Medical Informatics, 2016, 37(4): 8-13.
[101]	WENG H, LIU Z, YAN S, et al. A framework for automated knowledge graph construction towards traditional Chinese medicine[C]// LNCS 10594: Proceeding of the 2017 Inter-national Conference on Health Information Science. Cham: Springer, 2017: 170-181.
[102]	杨佳琦. 基于中文自然语言处理的糖尿病知识图谱构建[D]. 包头: 内蒙古科技大学, 2020.
	YANG J Q. Construction of diabetes knowledge graph based on Chinese natural language processing[D]. Baotou: Inner Mongolia University of Science & Technology, 2020.
[103]	刘勇, 齐梦霁. 基于糖尿病防治的医学知识图谱构建的研究[J]. 医学信息, 2020, 33(18): 11-14.
	LIU Y, QI M J. Research on the construction of medical knowledge graph based on diabetes prevention and treat-ment[J]. Journal of Medical Information, 2020, 33(18): 11-14.
[104]	HUANG Z S, YANG J, VAN HARMELEN F, et al. Con-structing knowledge graphs of depression[C]// LNCS 10594: Proceedings of the 6th International Conference on Health Information Science, Moscow, Oct 7-9, 2017. Cham: Springer, 2017: 149-161.
[105]	马欢欢. 基于电子病历的癫痫医学知识图谱构建的研究[D]. 曲阜: 曲阜师范大学, 2020.
	MA H H. Research on the construction of epilepsy medical knowledge graph based on electronic medical records[D]. Qufu: Qufu Normal University, 2020.
[106]	CHAI X. Diagnosis method of thyroid disease combining knowledge graph and deep learning[J]. IEEE Access, 2020, 8: 149787-149795. DOI URL
[107]	FANG A, LOU P, HU J, et al. Head and tail entity fusion model in medical knowledge graph construction: case study for pituitary adenoma[J]. JMIR Medical Informatics, 2021, 9(7): e28218. DOI URL
[108]	郑子强. 面向慢性肾脏病中医医案的知识图谱学习与推理研究[D]. 成都: 电子科技大学, 2020.
	ZHENG Z Q. Research on knowledge graph learning and reasoning for TCM prescription of chronic kidney disease[D]. Chengdu: University of Electronic Science and Tech-nology of China, 2020.
[109]	付洋, 刘茂福, 乔瑞. 心脏病中文知识图谱的构建[J]. 武汉大学学报(理学版), 2020, 66(3): 261-267.
	FU Y, LIU M F, QIAO R. Construction of Chinese know-ledge graph of heart disease[J]. Journal of Wuhan Univer-sity (Natural Science Edition), 2020, 66(3): 261-267.
[110]	杨帅, 王小红, 赵志刚, 等. COVID-19知识图谱构建与应用研究[J]. 青岛大学学报(工程技术版), 2021, 36(4): 22-29.
	YANG S, WANG X H, ZHAO Z G, et al. Research on the construction and application of COVID-19 knowledge graph[J]. Journal of Qingdao University (Engineering & Tech-nology Edition), 2021, 36(4): 22-29.
[111]	REUMANN M, GIOVANNINI A, NADWORNY B, et al. Cognitive DDx assistant in rare diseases[C]// Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Honolulu, Jul 18-21, 2018. Piscataway: IEEE, 2018: 3244-3247.
[112]	ZHU Q, NGUYEN D T, GRISHAGIN I, et al. An integra-tive knowledge graph for rare diseases, derived from the Genetic and Rare Diseases Information Center (GARD)[J]. Journal of Biomedical Semantics, 2020, 11(1): 1-13. DOI URL
[113]	于彤, 陈华钧, 李敬华. 面向中药新药研发的语义搜索系统[J]. 中国医学创新, 2013, 10(33): 152-154.
	YU T, CHEN H J, LI J H. A semantic search system for pharmaceutical manufacturing in traditional Chinese medi-cine[J]. Medical Innovation of China, 2013, 10(33): 152-154.
[114]	贾李蓉, 于彤, 崔蒙, 等. 中医药学语言系统研究进展[J]. 中国数字医学, 2014, 9(10): 57-59.
	JIA L R, YU T, CUI M, et al. Progress of research on the language system of traditional Chinese medicine[J]. China Digital Medicine, 2014, 9(10): 57-59.
[115]	WANG H L, ZHANG Q P, YUAN J H. Semantically en-hanced medical information retrieval system: a tensor fac-torization based approach[J]. IEEE Access, 2017, 5: 7584-7593. DOI URL
[116]	刘崇. 基于知识图谱的医疗知识搜索研究[D]. 杭州: 浙江理工大学, 2018.
	LIU C. Research of the medical knowledge based on know-ledge graph[D]. Hangzhou: Institutes of Technology of Zhejiang, 2018.
[117]	SINGH A V, NEGI A. Towards better drug repositioning using joint learning[C]// Proceedings of the 16th IEEE-India-Council International Conference, Rajkot, Dec 13-15, 2019. Piscataway: IEEE, 2019: 1-4.
[118]	PARK C, PARK J, PARK S. AGCN: attention-based graph convolutional networks for drug-drug interaction extraction[J]. Expert Systems with Applications, 2020, 159: 113538. DOI URL
[119]	GELETA D, NIKOLOV A, EDWARDS G, et al. Biological insights knowledge graph: an integrated knowledge graph to support drug development[J]. bioRxiv, 2021. DOI: 10. 1101/2021.10.28.466262. DOI
[120]	GENTILE A L, GRUHL D, RISTOSKI P, et al. Personalized knowledge graphs for the pharmaceutical domain[C]// LNCS 11779: Proceedings of the 18th International Semantic Web Conference, Auckland, Oct 26-30, 2019. Cham: Springer, 2019: 400-417.
[121]	王昊奋, 张金康, 程小军. 中文开放链接医疗数据的构建[J]. 中国数字医学, 2013, 8(4): 5-8.
	WANG H F, ZHANG J K, CHENG X J. The construction of Chinese open link medical data[J]. China Digital Me-dicine, 2013, 8(4): 5-8.
[122]	ZHAO T. An ontology-based decision support system for interventions based on monitoring medical conditions on patients in hospital wards[D]. Agder: University of Agder, 2014.
[123]	武家伟, 孙艳春. 融合知识图谱和深度学习方法的问诊推荐系统[J]. 计算机科学与探索, 2021, 15(8): 1432-1440. DOI
	WU J W, SUN Y C. Recommendation system for medical consultation integrating knowledge graph and deep learning method[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(8): 1432-1440.
[124]	FERRUCCI D, LEVAS A, BAGCHI S, et al. Watson: be-yond jeopardy![J]. Artificial Intelligence, 2013, 199: 93-105.
[125]	郑懿鸣, 翟洁, 胡晓龙, 等. 基于中医药知识图谱的智能问答与用药推荐系统[J]. 电子技术与软件工程, 2019(20): 134-135.
	ZHENG Y M, ZHAI J, HU X L, et al. Intelligent question answering and medication recommendation system based on the TCM knowledge graph[J]. Electronic Technology & Software Engineering, 2019(20): 134-135.
[126]	王继伟, 梁怀众, 樊伟, 等. 基于中文医疗知识图谱的智能问答系统设计与实现方法[J]. 中国数字医学, 2021, 16(2): 54-58.
	WANG J W, LIANG H Z, FAN W, et al. Design and im-plementation of intelligent Q&A system based on Chinese medical knowledge graph[J]. China Digital Medicine, 2021, 16(2): 54-58.
[127]	田迎, 单娅辉, 王时绘. 基于知识图谱的抑郁症自动问答系统研究[J]. 湖北大学学报(自然科学版), 2020, 42(5): 587-591.
	TIAN Y, SHAN Y H, WANG S H. The research of depre-ssion automatic question answering system based on know-ledge graph[J]. Journal of Hubei University (Natural Science), 2020, 42(5): 587-591.
[128]	任燕春, 赵瑛, 王铁, 等. 基于新冠肺炎知识图谱的智能问答系统研究[J]. 内蒙古科技大学学报, 2021, 40(3): 287-292.
	REN Y C, ZHAO Y, WANG T, et al. Intelligent question and answer system based in COVID-19 knowledge graph[J]. Journal of Inner Mongolia University of Science and Technology, 2021, 40(3): 287-292.
[129]	董佳琳, 张宇航, 徐永康, 等. 基于知识图谱的新冠疫情智能问答系统[J]. 信息技术与信息化, 2021(6): 258-261.
	DONG J L, ZHANG Y H, XU Y K, et al. COVID-19 intel-ligent question and answer system based on knowledge graph[J]. Information Technology and Informatization, 2021(6): 258-261.
[130]	LÓPEZ V, RHO V, BRISIMI T S, et al. Benefit graph extraction from healthcare policies[C]// LNCS 11779: Pro-ceedings of the 18th International Semantic Web Confer-ence, Auckland, Oct 26-30, 2019. Cham: Springer, 2019: 471-489.
[131]	黄智生, 胡青, 顾进广, 等. 网络智能机器人与自杀监控预警[J]. 中国数字医学, 2019, 14(3): 3-6.
	HUANG Z S, HU Q, GU J G, et al. Web-based intelligent agents for suicide monitoring and early warning[J]. China Digital Medicine, 2019, 14(3): 3-6.
[132]	崔佳伟. 肾细胞癌临床指南多层次知识建模与图谱化表示研究[D]. 北京: 北京协和医学院, 2020.
	CUI J W. Research on multi-level knowledge modeling and graphical representation of clinical guidelines for renal cell carcinoma[D]. Beijing: Peking Union Medical College, 2020.

中文医学知识图谱研究及应用进展

Research and Application Progress of Chinese Medical Knowledge Graph

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 2

参考文献 132

相关文章 7

编辑推荐

Metrics

[1]	萨日娜, 李艳玲, 林民. 知识图谱推理问答研究综述[J]. 计算机科学与探索, 2022, 16(8): 1727-1741.
[2]	夏光兵, 李瑞轩, 辜希武, 刘伟. 融合多源信息的知识表示学习[J]. 计算机科学与探索, 2022, 16(3): 591-597.
[3]	薛东前, 翟岩慧, 张少霞, 李德玉, 徐伟华. 决策蕴涵与可变决策蕴涵上的推理规则研究[J]. 计算机科学与探索, 2022, 16(10): 2357-2364.
[4]	李想, 杨兴耀, 于炯, 钱育蓉, 郑捷. 基于知识图谱卷积网络的双端推荐算法[J]. 计算机科学与探索, 2022, 16(1): 176-184.
[5]	舒世泰，李松，郝晓红，张丽平. 知识图谱嵌入技术研究进展[J]. 计算机科学与探索, 2021, 15(11): 2048-2062.
[6]	谢刚，吴高巍，任俊宏，张似衡，牛景昊，张文生. 面向患者的智能医生框架研究[J]. 计算机科学与探索, 2018, 12(9): 1475-1486.
[7]	王宝祥，周献中，盛寅，陆晓明，毛可. 面向人件服务的决策支持系统接口研究[J]. 计算机科学与探索, 2013, 7(1): 46-54.