Please wait a minute...
Advanced Search
数据分析与知识发现  2020, Vol. 4 Issue (12): 26-32
     专题 本期目录 | 过刊浏览 | 高级检索 |
复旦大学上海医学院 上海 200032
A Review of Medical Decision Supports Based on Knowledge Graph
Zhu Chaoyu,Liu Lei()
Shanghai Medical College, Fudan University, Shanghai 200032, China
全文: PDF (732 KB)   HTML ( 33
输出: BibTeX | EndNote (RIS)      

【目的】 对知识图谱推理在医学决策支持中的应用进行系统综述,为类似跨领域研究提供思路。【文献范围】 总计纳入参考文献39篇,其中知识图谱推理文献多来源于计算机会议,其他文献多以“知识图谱推理”和“医学决策支持”为关键字从Web of Science数据库筛选得到。【方法】 回顾医学决策支持发展历程,从传统医学到循证医学,再到计算机和医学知识图谱辅助的临床决策支持系统,其中医学知识图谱和知识图谱推理是实现以上发展转变的关键工具。【结果】 医学知识图谱与先进的知识图谱推理方法的结合可以极大地减轻临床医生的诊断压力,提高诊断效率,甚至减少误诊率。【局限】 以综述为主,没有对模型进行深入解析。【结论】 医学知识图谱就像是临床决策支持系统的大脑,而知识图谱推理则教会大脑如何运用这些知识。需要更为完整和准确的全科医学知识图谱,并不断改进知识图谱推理算法。

E-mail Alert
关键词 知识图谱推理医学决策支持临床决策支持系统    

[Objective] This paper systematically reviews the supporting applications for medical decisions based on knowledge graphs, aiming to expand similar interdisciplinary research. [Coverage] A total of 39 articles were retrieved from computer science conferences, as well as Web of Science with keywords of “knowledge graph reasoning” and “medical decision support”. [Methods] We reviewed the developments of medical decision support, from the perspectives of traditional and evidence-based medicine, as well as the computer and knowledge graph assisted systems. [Results] The medicine knowledge graph and reasoning significantly changed medical decision support systems, which also alleviated stress facing physicians, improved diagnosis efficiency, and reduced misdiagnosis. [Limitations] This article did not provide in-depth analysis of the reviewed models. [Conclusions] Medical knowledge graph is the “brain” of clinical decision support system, and knowledge graph reasoning helps the brain utilize relevant knowledge. We need to construct more comprehensive medical knowledge graphs, and improve their reasoning algorithms.

Key wordsKnowledge Graph Reasoning    Medical Decision Support    Clinical Decision Support System
收稿日期: 2020-09-27      出版日期: 2020-12-25
ZTFLH:  TP399  
通讯作者: 刘雷     E-mail:
朱超宇, 刘雷. 基于知识图谱的医学决策支持应用综述*[J]. 数据分析与知识发现, 2020, 4(12): 26-32.
Zhu Chaoyu, Liu Lei. A Review of Medical Decision Supports Based on Knowledge Graph. Data Analysis and Knowledge Discovery, 2020, 4(12): 26-32.
链接本文:      或
Fig.1  生物医学实体知识图谱模式[30]
Fig.2  双重驱动临床决策支持平台结构[33]
Fig.3  知识图谱辅助肺部影像诊断系统结构
  Summary of Medical Knowledge Graph
[1] Bollacker K, Evans C, Paritosh P, et al. Freebase: A Collaboratively Created Graph Database for Structuring Human Knowledge [C]//Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. 2008: 1247-1250.
[2] 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.
[3] The Gene Ontology Consortium. The Gene Ontology Resource: 20 Years and Still Going Strong[J]. Nucleic Acids Research, 2019,47:D330-D338.
doi: 10.1093/nar/gky1055 pmid: 30395331
[4] Wishart D S, Feunang Y D, Guo A C , et al. DrugBank 5.0: A Major Update to the DrugBank Database for 2018[J]. Nucleic Acids Research, 2018,46:D1074-D1982.
pmid: 29126136
[5] UniProt Consortium . UniProt: A Worldwide Hub of Protein Knowledge[J]. Nucleic Acids Research, 2019,47:D506-D515.
doi: 10.1093/nar/gky1049 pmid: 30395287
[6] Lin Y K, Liu Z Y, Sun M S, et al. Learning Entity and Relation Embeddings for Knowledge Graph Completion [C]// Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015: 2181-2187.
[7] Bordes A, Chopra S, Weston J. Question Answering with Subgraph Embeddings [C] // Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 2014: 615-620.
[8] Lao N, Mitchell T, Cohen W. Random Walk Inference and Learning in a Large Scale Knowledge Base [C] // Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. 2011: 529-539.
[9] Cohen W. Learning Relational Features with Backward Random Walks [C] // Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics. 2015: 666-675.
[10] Mitchell T. Efficient and Expressive Knowledge Base Completion Using Subgraph Feature Extraction [C] // Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015: 1488-1498.
[11] Bordes A, Usunier N, Garciaduran A, et al. Translating Embeddings for Modeling Multi-Relational Data [C]//Proceedings of the 26th International Conference on Neural Information Processing Systems. 2013: 2787-2795.
[12] 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. 2014: 1112-1119.
[13] Jenatton R, Roux N L, Bordes A, et al. A Latent Factor Model for Highly Multi-Relational Data [C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. 2012: 3167-3175.
[14] Nickel M, Rosasco L, Poggio T. Holographic Embeddings of Knowledge Graphs [C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015: 1955-1961.
[15] Trouillon T, Welbl J, Riedel S, et al. Complex Embeddings for Simple Link Prediction [C]//Proceedings of the 33rd International Conference on International Conference on Machine Learning. 2016: 2071-2080.
[16] Dettmers T, Minervini P, Stenetorp P, et al. Convolutional 2D Knowledge Graph Embeddings [C]//Proceedings of the 31st AAAI Conference on Artificial Intelligence. 2017: 1811-1818.
[17] Shi B X, Weninger T. ProjE: Embedding Projection for Knowledge Graph Completion [C]//Proceedings of the 31st AAAI Conference on Artificial Intelligence. 2017: 1236-1242.
[18] Nguyen D, Nguyen T, Nguyen D Q, et al. A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network [C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. 2018: 327-333.
[19] Ferrucci D A, Brown E W, Chucarroll J , et al. Building Watson: An Overview of the DeepQA Project[J]. AI Magazine, 2010,31(3):59-79.
[20] Mohammed S, Shi P, Lin J. Strong Baselines for Simple Question Answering over Knowledge Graphs with and Without Neural Networks [C] // Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 2017: 291-296.
[21] Petrochuk M, Zettlemoyer L. SimpleQuestions Nearly Solved: A New Upperbound and Baseline Approach [C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018: 554-558.
[22] Evans J S B T. Dual-Processing Accounts of Reasoning, Judgment, and Social Cognition[J]. Annual Review of Psychology, 2008,59(1):255-278.
[23] Ding M, Zhou C, Chen Q B, et al. Cognitive Graph for Multi-Hop Reading Comprehension at Scale [C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019: 2694-2703.
[24] Devlin J, Chang M W, Lee K, et al. BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding [C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019: 4171-4186.
[25] Mohamed S K, Novacek V, Nounu A . Discovering Protein Drug Targets Using Knowledge Graph Embeddings[J]. Bioinformatics, 2019,36(2):603-610.
pmid: 31368482
[26] Yamanishi Y, Araki M, Gutteridge A , et al. Prediction of Drug-Target Interaction Networks from the Integration of Chemical and Genomic Spaces[J]. Bioinformatics, 2008,24(13):232-240.
[27] Kanehisa M, Furumichi M, Tanabe M , et al. KEGG: New Perspectives on Genomes, Pathways, Diseases and Drugs[J]. Nucleic Acids Research, 2017,45:D353-D361.
pmid: 27899662
[28] Liang X M, Li D F, Song M , et al. Predicting Biomedical Relationships Using the Knowledge and Graph Embedding Cascade Model[J]. PLoS One, 2019,14(6):e0218264.
doi: 10.1371/journal.pone.0218264 pmid: 31194807
[29] Zhao D, Wang J, Sang S T , et al. Relation Path Feature Embedding Based Convolutional Neural Network Method for Drug Discovery[J]. BMC Medical Informatics and Decision Making, 2019,19(2):121-130.
[30] Mohamed S K, Nounu A, Novacek V . Biological Applications of Knowledge Graph Embedding Models[J]. Briefings in Bioinformatics, DOI: 10.1093/bib/bbaa012.
doi: 10.1093/bib/bbaa361 pmid: 33326994
[31] Goodwin T R, Harabagiu S M. Medical Question Answering for Clinical Decision Support [C]//Proceedings of the 25th ACM International Conference on Information and Knowledge Management. 2016: 297-306.
[32] Zhao C, Jiang J C, 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: 10.1016/j.artmed.2018.03.005 pmid: 29691122
[33] Sheng M, Hu Q C, Zhang Y, et al. A Data-Intensive CDSS Platform Based on Knowledge Graph [C]//Proceedings of the 7th International Conference on Health Information Science. 2018: 146-155.
[34] Xie Y T, Zhang J P, Xia Y . Semi-Supervised Adversarial Model for Benign-Malignant Lung Nodule Classification on Chest CT[J]. Medical Image Analysis, 2019,57:237-248.
[35] Majkowska A, Mittal S, Steiner D , et al. Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-Adjudicated Reference Standards and Population-Adjusted Evaluation[J]. Radiology, 2020,294(2):421-431.
doi: 10.1148/radiol.2019191293 pmid: 31793848
[36] Zhang K, Liu X H, Shen J , et al. Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography[J]. Cell, 2020,181(6):1423-1433.
doi: 10.1016/j.cell.2020.04.045 pmid: 32416069
[37] Ashley E A . Towards Precision Medicine[J]. Nature Reviews Genetics, 2016,17(9):507-522.
doi: 10.1038/nrg.2016.86 pmid: 27528417
[38] Kohler S, Carmody L C, Vasilevsky N , et al. Expansion of the Human Phenotype Ontology (HPO) Knowledge Base and Resources[J]. Nucleic Acids Research, 2019,47:1018-1027.
[39] Latorre-Pellicer A, Ascaso A, Trujillano L , et al. Evaluating Face2Gene as a Tool to Identify Cornelia de Lange Syndrome by Facial Phenotypes[J]. International Journal of Molecular Sciences, 2020,21(3):1042.
[1] 阮小芸,廖健斌,李祥,杨阳,李岱峰. 基于人才知识图谱推理的强化学习可解释推荐研究*[J]. 数据分析与知识发现, 2021, 5(6): 36-50.
Full text



版权所有 © 2015 《数据分析与知识发现》编辑部
地址:北京市海淀区中关村北四环西路33号 邮编:100190