[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.
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.
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.
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.
The Gene Ontology Consortium. The Gene Ontology Resource: 20 Years and Still Going Strong[J]. Nucleic Acids Research, 2019,47:D330-D338.
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.
UniProt Consortium . UniProt: A Worldwide Hub of Protein Knowledge[J]. Nucleic Acids Research, 2019,47:D506-D515.
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.
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.
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.
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.
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.
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.
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.
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.
Nickel M, Rosasco L, Poggio T. Holographic Embeddings of Knowledge Graphs [C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015: 1955-1961.
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.
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.
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.
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.
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.
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.
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.
Evans J S B T. Dual-Processing Accounts of Reasoning, Judgment, and Social Cognition[J]. Annual Review of Psychology, 2008,59(1):255-278.
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.
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.
Mohamed S K, Novacek V, Nounu A . Discovering Protein Drug Targets Using Knowledge Graph Embeddings[J]. Bioinformatics, 2019,36(2):603-610.
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.
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.
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.
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.
Mohamed S K, Nounu A, Novacek V . Biological Applications of Knowledge Graph Embedding Models[J]. Briefings in Bioinformatics, DOI: 10.1093/bib/bbaa012.
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.
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.
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.
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.
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.
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.
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.
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.