Home Big Data Mining and Analytics Article
PDF (552.9 KB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Figures (1)

Tables (3)
Table 1
Table 2
Table 3
Open Access

Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Xuehong Wu1,2,†, Junwen Duan1,†, Yi Pan3, Min Li1()
School of Computer Science and Engineering, Central South University, Changsha 410083, China
School of Computer Science, Hunan First Normal University, Changsha 410006, China
Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China

† Xuehong Wu and Junwen Duan contribute equally to this work.

Show Author Information

Abstract

Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have been in use in a variety of intelligent medical applications. Thus, understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field. To this end, we offer an in-depth review of MKG in this work. Our research begins with the examination of four types of medical information sources, knowledge graph creation methodologies, and six major themes for MKG development. Furthermore, three popular models of reasoning from the viewpoint of knowledge reasoning are discussed. A reasoning implementation path (RIP) is proposed as a means of expressing the reasoning procedures for MKG. In addition, we explore intelligent medical applications based on RIP and MKG and classify them into nine major types. Finally, we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

Keywords

medical knowledge graphknowledge graph constructionknowledge reasoningintelligent medical applicationsintelligent healthcare

References

[1]
A. Singhal, Introducing the knowledge graph: Things, not strings, https://blog.google/products/search/introducing-knowledge-graph-things-not/, 2012.
[2]
J. Wang, X. Wang, C. Ma, and L. Kou, A survey on the development status and application prospects of knowledge graph in smart grids, IET Gener. Transm. Distrib., vol. 15, no. 3, pp. 383–407, 2021.
Crossref Google Scholar
[3]
E. I. Papageorgiou, C. Huszka, J. De Roo, N. Douali, M. C. Jaulent, and D. Colaert, Application of probabilistic and fuzzy cognitive approaches in semantic web framework for medical decision support, Comput. Methods Programs Biomed., vol. 112, no. 3, pp. 580–598, 2013.
Crossref Google Scholar
[4]
T. Yu, J. Li, Q. Yu, Y. Tian, X. Shun, L. Xu, L. Zhu, and H. Gao, Knowledge graph for TCM health preservation: Design, construction, and applications, Artif. Intell. Med., vol. 77, pp. 48–52, 2017.
Crossref Google Scholar
[5]
P. Ping, K. Watson, J. Han, and A. Bui, Individualized knowledge graph: A viable informatics path to precision medicine, Circ. Res., vol. 120, no. 7, pp. 1078–1080, 2017.
Crossref Google Scholar
[6]
D. M. Bean, H. Wu, E. Iqbal, O. Dzahini, Z. M. Ibrahim, M. Broadbent, R. Stewart, and R. J. B. Dobson, Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records, Sci. Rep., vol. 7, no. 1, p. 16416, 2017.
Crossref Google Scholar
[7]
G. Bakal, P. Talari, E. V. Kakani, and R. Kavuluru, Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations, J. Biomed. Inform., vol. 82, pp. 189–199, 2018.
Crossref Google Scholar
[8]
Y. Dai, C. Guo, W. Guo, and C. Eickhoff, Drug-drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings, Brief. Bioinform., vol. 22, no. 4, p. bbaa256, 2020.
Crossref Google Scholar
[9]
H. Xu, J. Li, L. Nie, and X. Xu, CKGECS: A Chinese knowledge graph for elderly care service, Int.J. Serv. Technol. Manag., vol. 26, nos. 2&3, pp. 131–148, 2020.
Crossref Google Scholar
[10]
C. Chen, K. E. Ross, S. Gavali, J. E. Cowart, and C. H. Wu, COVID-19 knowledge graph from semantic integration of biomedical literature and databases, Bioinformatics, vol. 37, no. 23, pp. 4597–4598, 2021.
Crossref Google Scholar
[11]
Q. Zhao, J. Li, C. Xu, J. Yang, and L. Zhao, Knowledge-enhanced relation extraction for Chinese EMRs, IT Prof., vol. 22, no. 4, pp. 57–62, 2020.
Crossref Google Scholar
[12]
T. D. Gunter and N. P. Terry, The emergence of national electronic health record architectures in the United States and Australia: Models, costs, and questions, J. Med. Internet Res., vol. 7, no. 1, p. e3, 2005.
Crossref Google Scholar
[13]
Z. Huang, Q. Hu, M. Liao, C. Miao, C. Wang, and G. Liu, Knowledge graphs of Kawasaki disease, Health Inf. Sci. Syst., vol. 9, no. 1, p. 11, 2021.
Crossref Google Scholar
[14]
X. Tao, T. Pham, J. Zhang, J. Yong, W. P. Goh, W. Zhang, and Y. Cai, Mining health knowledge graph for health risk prediction, World Wide Web, vol. 23, no. 4, pp. 2341–2362, 2020.
Crossref Google Scholar
[15]
S. M. S. Hasan, D. Rivera, X. C. Wu, E. B. Durbin, J. B. Christian, and G. Tourassi, Knowledge graph-enabled cancer data analytics, IEEE J. Biomed. Health Inform., vol. 24, no. 7, pp. 1952–1967, 2020.
Crossref Google Scholar
[16]
C. E. Lipscomb, Medical Subject Headings (MeSH), Bull. Med. Libr. Assoc., vol. 88, no. 3, pp. 265–266, 2000.
Google Scholar
[17]
K. Donnelly, SNOMED-CT: The advanced terminology and coding system for eHealth, Stud. Health Technol. Inform., vol. 121, pp. 279–290, 2006.
Google Scholar
[18]
O. Bodenreider, The Unified Medical Language System (UMLS): Integrating biomedical terminology, Nucl. Acids Res., vol. 32, no. S1, pp. D267-D270, 2004.
Crossref Google Scholar
[19]
D. S. Wishart, C. Knox, A. C. Guo, S. Shrivastava, M. Hassanali, P. Stothard, Z. Chang, and J. Woolsey, DrugBank: A comprehensive resource for in silico drug discovery and exploration, Nucl. Acids Res., vol. 34, pp. D668–D672, 2006.
Crossref Google Scholar
[20]
M. Kanehisa, M. Furumichi, M. Tanabe, Y. Sato, and K. Morishima, KEGG: New perspectives on genomes, pathways, diseases and drugs, Nucl. Acids Res., vol. 45, no. D1, pp. D353–D361, 2017.
Crossref Google Scholar
[21]
J. Xie, J. Jiang, Y. Wang, Y. Guan, and X. Guo, Learning an expandable EMR-based medical knowledge network to enhance clinical diagnosis, Artif. Intell. Med., vol. 107, p. 101927, 2020.
Crossref Google Scholar
[22]
Y. Zhang, M. Sheng, R. Zhou, Y. Wang, G. Han, H. Zhang, C. Xing, and J. Dong, HKGB: An inclusive, extensible, intelligent, semi-auto-constructed knowledge graph framework for healthcare with clinicians’ expertise incorporated, Inform. Proc. Manag., vol. 57, no. 6, p. 1023246, 2020.
Crossref Google Scholar
[23]
K. M. Malik, M. Krishnamurthy, M. Alobaidi, M. Hussain, F. Alam, and G. Malik, Automated domain-specific healthcare knowledge graph curation framework: Subarachnoid hemorrhage as phenotype, Expert Syst. Appl., vol. 145, p. 113120, 2020.
Crossref Google Scholar
[24]
M. A. Musen, The protégé project: A look back and a look forward, AI Matt., vol. 1, no. 4, pp. 4–12, 2015.
Crossref Google Scholar
[25]
M. Alobaidi, K. M. Malik, and M. Hussain, Automated ontology generation framework powered by linked biomedical ontologies for disease-drug domain, Comput. Methods Programs Biomed., vol. 165, pp. 117–128, 2018.
Crossref Google Scholar
[26]
S. Feng, H. Ning, S. Yang, and D. Zhao, Geriatric disease reasoning based on knowledge graph, in Int. 2019 Cyberspace Congress, CyberDI and CyberLife on Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health, Beijing, China, 2019, pp. 452–465.
Crossref Google Scholar
[27]
D. Korn, T. Bobrowski, M. Li, Y. Kebede, P. Wang, P. Owen, G. Vaidya, E. Muratov, R. Chirkova, C. Bizon, et al., COVID-KOP: Integrating Emerging COVID-19 Data with the ROBOKOP Database, Bioinformatics, vol. 37, no. 4, pp. 586–587, 2021.
Crossref Google Scholar
[28]
F. Zhang, B. Sun, X. Diao, W. Zhao, and T. Shu, Prediction of adverse drug reactions based on knowledge graph embedding, BMC Med. Inform. Decis. Mak., vol. 21, no. 1, p. 38, 2021.
Crossref Google Scholar
[29]
K. Zhang, K. Li, H. Ma, D. Yue, and L. Zhuang, Construction of MeSH-like obstetric knowledge graph, in Proc. 2018 Int. Conf. Cyber-Enabled Distributed Computing and Knowledge Discovery, New York, NY, USA, 2018, pp. 160–168.
Crossref Google Scholar
[30]
C. Zhao, J. Jiang, Y. Guan, X. Guo, and B. He, EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning, Artif. Intell. Med., vol. 87, pp. 49–59, 2018.
Crossref Google Scholar
[31]
M. R. Kamdar, C. E. Stanley Jr, M. Carroll, L. Wogulis, W. Dowling, H. F. Deus, and M. Samarasinghe, Text snippets to corroborate medical relations: An unsupervised approach using a knowledge graph and embeddings, AMIA Jt. Summits Transl. Sci. Proc., vol. 2020, pp. 288–297, 2020.
Google Scholar
[32]
F. Michel, F. Gandon, V. Ah-Kane, A. Bobasheva, E. Cabrio, O. Corby, R. Gazzotti, A. Giboin, S. Marro, T. Mayer, et al., Covid-on-the-Web: Knowledge graph and services to advance COVID-19 research, in 19th Int. Semantic Web Conf. on the Semantic Web, Athens, Greece, 2020, pp. 294–310.
Crossref Google Scholar
[33]
P. Liu, X. Wang, X. Sun, X. Shen, X. Chen, Y. Sun, and Y. Pan, HKDP: A hybrid knowledge graph based pediatric disease prediction system, in Int. Conf. on Smart Health, Haikou, China, 2017, pp. 78–90.
Crossref Google Scholar
[34]
X. Li, Y. Wang, D. Wang, W. Yuan, D. Peng, and Q. Mei, Improving rare disease classification using imperfect knowledge graph, BMC Med. Inform. Decis. Mak., vol. 19, no. 5, p. 238, 2019.
Crossref Google Scholar
[35]
A. Ettorre, O. R. Rodríguez, C. Faron, F. Michel, and F. Gandon, A knowledge graph enhanced learner model to predict outcomes to questions in the medical field, in 22n⁢d Int. Conf. on Knowledge Engineering and Knowledge Management, Bolzano, Italy, 2020, pp. 237–251.
Crossref Google Scholar
[36]
S. Ansong, K. F. Eteffa, C. Li, M. Sheng, Y. Zhang, and C. Xing, How to empower disease diagnosis in a medical education system using knowledge graph, in 16th Int. Conf. on Web Information Systems and Applications, Qingdao, China, 2019, pp. 518–523.
Crossref Google Scholar
[37]
M. Wang, X. Ma, J. Si, H. Tang, H. Wang, T. Li, W. Ouyang, L. Gong, Y. Tang, and X. He, et al., Adverse drug reaction discovery using a tumor-biomarker knowledge graph, Front. Genet., vol. 11, p. 625659, 2021.
Crossref Google Scholar
[38]
I. Abdelaziz, A. Fokoue, O. Hassanzadeh, P. Zhang, and M. Sadoghi, Large-scale structural and textual similarity-based mining of knowledge graph to predict drug–drug interactions, J. Web Semant., vol. 44, pp. 104–117, 2017.
Crossref Google Scholar
[39]
M. Sheng, Q. Hu, Y. Zhang, C. Xing, and T. Zhang, A data-intensive CDSS platform based on knowledge graph, in 7th Int. Conf. on Health Information Science, Cairns, Australia, 2018, pp. 146–155.
Crossref Google Scholar
[40]
F. Song, B. Wang, Y. Tang, and J. Sun, Research of medical aided diagnosis system based on temporal knowledge graph, in 16t⁢h Int. Conf. on Advanced Data Mining and Applications, Foshan, China, 2020, pp. 236–250.
Crossref Google Scholar
[41]
Q. Bao, L. Ni, and J. Liu, HHH: An online medical chatbot system based on knowledge graph and hierarchical bi-directional attention, in Proc. Australasian Computer Science Week Multiconference, Melbourne, Australia, 2020, pp. 1–10.
Crossref Google Scholar
[42]
Z. Jiang, C. Chi, and Y. Zhan, Research on medical question answering system based on knowledge graph, IEEE Access, vol. 9, pp. 21094–21101, 2021.
Crossref Google Scholar
[43]
Y. Duan, P. Ji, L. Jin, A. Zou, J. Yang, H. Xie, and N. An, A knowledge graph for eldercare: Constructing a domain entity graph with guidelines, in 4th Int. Conf. on Human Aspects of IT for the Aged Population. Applications in Health, Assistance, and Entertainment, Vegas, NV, USA, 2018, pp. 25–35.
Crossref Google Scholar
[44]
X. He, R. Zhang, R. Rizvi, J. Vasilakes, X. Yang, Y. Guo, Z. He, M. Prosperi, J. Huo, J. Alpert, et al., ALOHA: Developing an interactive graph-based visualization for dietary supplement knowledge graph through user-centered design, BMC Med. Inform. Decis. Mak., vol. 19, no. 4, p. 150, 2019.
Crossref Google Scholar
[45]
H. Sun, J. Xiao, W. Zhu, Y. He, S. Zhang, X. Xu, L. Hou, J. Li, Y. Ni, and G. Xie, Medical knowledge graph to enhance fraud, waste, and abuse detection on claim data: Model development and performance evaluation, JMIR Med. Inform., vol. 8, no. 7, p. e17653, 2020.
Crossref Google Scholar
[46]
T. Pham, X. Tao, J. Zhang, and J. Yong, Constructing a knowledge-based heterogeneous information graph for medical health status classification, Health Inf. Sci. Syst., vol. 8, no. 1, p. 10, 2020.
Crossref Google Scholar
[47]
L. Li, P. Wang, J. Yan, Y. Wang, S. Li, J. Jiang, Z. Sun, B. Tang, T. H. Chang, S. Wang, et al., Real-world data medical knowledge graph: Construction and applications, Artif. Intell. Med., vol. 103, p. 101817, 2020.
Crossref Google Scholar
[48]
M. Wang, J. Zhang, J. Liu, W. Hu, S. Wang, X. Li, and W. Liu, PDD Graph: Bridging electronic medical records and biomedical knowledge graphs via entity linking, in 16th Int. Semantic Web Conf. on the Semantic Web, Vienna, Austria, 2017, pp. 219–227.
Crossref Google Scholar
[49]
X. Zhang and C. Che, Drug repurposing for parkinson’s disease by integrating knowledge graph completion model and knowledge fusion of medical literature, Future Internet, vol. 13, no. 1, p. 14, 2021.
Crossref Google Scholar
[50]
P. Ernst, A. Siu, and G. Weikum, KnowLife: A versatile approach for constructing a large knowledge graph for biomedical sciences, BMC Bioinform., vol. 16, no. 1, p. 157, 2015.
Crossref Google Scholar
[51]
M. A. Patil, S. Bhaumik, S. Paul, S. Bissoyi, R. Roy, and S. Ryu, Estimating personalized risk ranking using laboratory test and medical knowledge (UMLS), in 2013 35th Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 2013, pp. 1274–1277.
Crossref Google Scholar
[52]
K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor, Freebase: A collaboratively created graph database for structuring human knowledge, in Proc. 2008 ACM SIGMOD Int. Conf. Management of Data, Vancouver, Canada, 2008, pp. 1247–1250.
Crossref Google Scholar
[53]
A. S. Brown and C. J. Patel, A standard database for drug repositioning, Sci. Data, vol. 4, no. 1, p. 170029, 2017.
Crossref Google Scholar
[54]
H. Kilicoglu, D. Shin, M. Fiszman, G. Rosemblat, and T. C. Rindflesch, SemMedDB: A PubMed-scale repository of biomedical semantic predications, Bioinformatics, vol. 28, no. 23, pp. 3158–3160, 2012.
Crossref Google Scholar
[55]
F. Teng, W. Yang, L. Chen, L. Huang, and Q. Xu, Explainable prediction of medical codes with knowledge graphs, Front. Bioeng. Biotechnol., vol. 8, p. 867, 2020.
Crossref Google Scholar
[56]
T. B. Malas, W. J. Vlietstra, R. Kudrin, S. Starikov, M. Charrout, M. Roos, D. J. M. Peters, J. A. Kors, R. Vos, P. A. C. ‘t Hoen, et al., Drug prioritization using the semantic properties of a knowledge graph, Sci. Rep., vol. 9, no. 1, p. 6281, 2019.
Crossref Google Scholar
[57]
C. Bizon, S. Cox, J. Balhoff, Y. Kebede, P. Wang, K. Morton, K. Fecho, and A. Tropsha, ROBOKOP KG and KGB: Integrated knowledge graphs from federated sources, J. Chem. Inf. Model., vol. 59, no. 12, pp. 4968–4973, 2019.
Crossref Google Scholar
[58]
T. R. Goodwin and S. M. Harabagiu, Medical question answering for clinical decision support, Proc. ACM Int. Conf. Inf. Knowl. Manag., vol. 2016, pp. 297–306, 2016.
Crossref Google Scholar
[59]
M. Rotmensch, Y. Halpern, A. Tlimat, S. Horng, and D. Sontag, Learning a health knowledge graph from electronic medical records, Sci. Rep., vol. 7, no. 1, p. 5994, 2017.
Crossref Google Scholar
[60]
B. Cheng, Y. Zhang, D. Cai, W. Qiu, and D. Shi, Construction of traditional Chinese medicine knowledge graph using data mining and expert knowledge, in Proc. 2018 IEEE Int. Conf. Network Infrastructure and Digital Content, Guiyang, China, 2018, pp. 209–213.
Crossref Google Scholar
[61]
M. Sheng, Y. Shao, Y. Zhang, C. Li, C. Xing, H. Zhang, J. Wang, and F. Gao, DEKGB: An extensible framework for health knowledge graph, in International Conference on Smart Health, Shenzhen, China, 2019, pp. 27–38.
Crossref Google Scholar
[62]
L. Li, P. Wang, Y. Wang, S. Wang, J. Yan, J. Jiang, B. Tang, C. Wang, and Y. Liu, A method to learn embedding of a probabilistic medical knowledge graph: Algorithm development, JMIR Med. Inform., vol. 8, no. 5, p. e17645, 2020.
Crossref Google Scholar
[63]
L. Wang, H. Xie, W. Han, X. Yang, L. Shi, J. Dong, K. Jiang, and H. Wu, Construction of a knowledge graph for diabetes complications from expert-reviewed clinical evidences, Comput. Assist. Surg., vol. 25, no. 1, pp. 29–35, 2020.
Crossref Google Scholar
[64]
M. Sheng, A. Li, Y. Bu, J. Dong, Y. Zhang, X. Li, C. Li, and C. Xing, DSQA: A domain specific QA system for smart health based on knowledge graph, in 17th Int. Conf. on Web Information Systems and Applications, Guangzhou, China, 2020, pp. 215–222.
Crossref Google Scholar
[65]
X. Li, H. Liu, X. Zhao, G. Zhang, and C. Xing, Automatic approach for constructing a knowledge graph of knee osteoarthritis in Chinese, Health Inf. Sci. Syst., vol. 8, no. 1, p. 12, 2020.
Crossref Google Scholar
[66]
F. Gong, M. Wang, H. Wang, S. Wang, and M. Liu, SMR: Medical knowledge graph embedding for safe medicine recommendation, Big Data Res., vol. 23, p. 100174, 2021.
Crossref Google Scholar
[67]
Z. Lin, D. Yang, and X. Yin, Patient similarity via joint embeddings of medical knowledge graph and medical entity descriptions, IEEE Access, vol. 8, pp. 156663–156676, 2020.
Crossref Google Scholar
[68]
W. Deng, P. Guo, and J. Yang, Medical entity extraction and knowledge graph construction, in 2019 16th Int. Computer Conf. Wavelet Active Media Technology and Information Processing, Chengdu, China, 2019, pp. 41–44.
Crossref Google Scholar
[69]
T. Ruan, Y. Huang, X. Liu, Y. Xia, and J. Gao, QAnalysis: A question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research, BMC Med. Inform. Decis. Mak., vol. 19, no. 1, p. 82, 2019.
Crossref Google Scholar
[70]
W. Van Woensel, C. Armstrong, M. Rajaratnam, V. Gupta, and S. S. R. Abidi, Using knowledge graphs to plausibly infer missing associations in EMR data, Stud. Health Technol. Inform., vol. 281, pp. 417–421, 2021.
Crossref Google Scholar
[71]
A. Fang, P. Lou, J. Hu, W. Zhao, M. Feng, H. Ren, and X. Chen, Head and tail entity fusion model in medical knowledge graph construction: Case study for pituitary adenoma, JMIR Med. Inform., vol. 9, no. 7, p. e28218, 2021.
Crossref Google Scholar
[72]
A. K. Talukder, J. B. Sanz, and J. Samajpati, ‘Precision Health’: Balancing reactive care and proactive care through the evidence based knowledge graph constructed from real-world electronic health records, disease trajectories, diseasome, and patholome, in 8th Int. Conf. on Big Data Analytics, Sonepat, India, 2020, pp. 113–133.
Crossref Google Scholar
[73]
I. Y. Chen, M. Agrawal, S. Horng, and D. Sontag, Robustly extracting medical knowledge from EHRs: A case study of learning a health knowledge graph, Pac. Symp. Biocomput., vol. 25, pp. 19–30, 2020.
Google Scholar
[74]
L. Shi, S. Li, X. Yang, J. Qi, G. Pan, and B. Zhou, Semantic health knowledge graph: Semantic integration of heterogeneous medical knowledge and services, Biomed. Res. Int., vol. 2017, p. 2858423, 2017.
Crossref Google Scholar
[75]
J. Lee, C. Liu, J. H. Kim, A. Butler, N. Shang, C. Pang, K. Natarajan, P. Ryan, C. Ta, and C. Weng, Comparative effectiveness of medical concept embedding for feature engineering in phenotyping, JAMIA Open, vol. 4, no. 2, p. ooab028, 2021.
Crossref Google Scholar
[76]
K. He, L. Yao, J. Zhang, Y. Li, and C. Li, Construction of genealogical knowledge graphs from obituaries: Multitask neural network extraction system, J. Med. Internet. Res., vol. 23, no. 8, p. e25670, 2021.
Crossref Google Scholar
[77]
H. Weng, Z. Liu, S. Yan, M. Fan, A. Ou, D. Chen, and T. Hao, A framework for automated knowledge graph construction towards traditional Chinese medicine, in 6th Int. Conf. on Health Information Science, Moscow, Russia, 2017, pp. 170–181.
Crossref Google Scholar
[78]
J. Du and X. Li, A knowledge graph of combined drug therapies using semantic predications from biomedical literature: Algorithm development, JMIR Med. Inform., vol. 8, no. 4, p. e18323, 2020.
Crossref Google Scholar
[79]
Z. Huang, J. Yang, F. van Harmelen, and Q. Hu, Constructing knowledge graphs of depression, in lecture notes in computer science, in 6th Int. Conf. on Health Information Science, Moscow, Russia, 2017, pp. 149–161.
Crossref Google Scholar
[80]
S. Sang, Z. Yang, L. Wang, X. Liu, H. Lin, and J. Wang, SemaTyP: A knowledge graph based literature mining method for drug discovery, BMC Bioinform., vol. 19, no. 1, p. 193, 2018.
Crossref Google Scholar
[81]
J. Yuan, Z. Jin, H. Guo, H. Jin, X. Zhang, T. Smith, and J. Luo, Constructing biomedical domain-specific knowledge graph with minimum supervision, Knowl. Inf. Syst., vol. 62, no. 1, pp. 317–336, 2020.
Crossref Google Scholar
[82]
W. J. Vlietstra, R. Zielman, R. M. van Dongen, E. A. Schultes, F. Wiesman, R. Vos, E. M. van Mulligen, and J. A. Kors, Automated extraction of potential migraine biomarkers using a semantic graph, J. Biomed. Inform., vol. 71, pp. 178–189, 2017.
Crossref Google Scholar
[83]
A. Rossanez, J. C. dos Reis, R. da Silva Torres, and H. de Ribaupierre, KGen: A knowledge graph generator from biomedical scientific literature, BMC Med. Inform. Decis. Mak., vol. 20, no. 4, p. 314, 2020.
Crossref Google Scholar
[84]
D. N. Sosa, A. Derry, M. Guo, E. Wei, C. Brinton, and R. B. Altman, A literature-based knowledge graph embedding method for identifying drug repurposing opportunities in rare diseases, Pac. Symp. Biocomput., vol. 25, pp. 463–474, 2020.
Crossref Google Scholar
[85]
Y. Song, L. Cai, K. Zhang, H. Zan, T. Liu, and X. Ren, Construction of Chinese pediatric medical knowledge graph, in 9th Joint Int. Conf. on Semantic Technology, Hangzhou, China, 2020, pp. 213–220.
Crossref Google Scholar
[86]
G. Bakal and R. Kavuluru, Predicting treatment relations with semantic patterns over biomedical knowledge graphs, in 3rd Int. Conf. on Mining Intelligence and Knowledge Exploration, Hyderabad, India, 2015, pp. 586–596.
Crossref Google Scholar
[87]
H. Fei, Y. Ren, Y. Zhang, D. Ji, and X. Liang, Enriching contextualized language model from knowledge graph for biomedical information extraction, Brief. Bioinform., vol. 22, no. 3, p. bbaa110, 2021.
Crossref Google Scholar
[88]
D. Zhao, J. Wang, S. Sang, H. Lin, J. Wen, and C. Yang, Relation path feature embedding based convolutional neural network method for drug discovery, BMC Med. Inform. Decis. Mak., vol. 19, no. 2, p. 59, 2019.
Crossref Google Scholar
[89]
C. T. Hoyt, D. Domingo-Fernández, R. Aldisi, L. Xu, K. Kolpeja, S. Spalek, E. Wollert, J. Bachman, B. M. Gyori, P. Greene, et al., Re-curation and rational enrichment of knowledge graphs in biological expression language, Database, vol. 2019, p. baz068, 2019.
Crossref Google Scholar
[90]
T. Pham, X. Tao, J. Zhang, J. Yong, X. Zhou, and R. Gururajan, MeKG: Building a medical knowledge graph by data mining from MEDLINE, in 12th Int. Conf. on Brain Informatics, Haikou, China, 2019, pp. 159–168.
Crossref Google Scholar
[91]
X. Yang, C. Wu, G. Nenadic, W. Wang, and K. Lu, Mining a stroke knowledge graph from literature, BMC Bioinform., vol. 22, no. 10, p. 387, 2021.
Crossref Google Scholar
[92]
X. Chai, Diagnosis method of thyroid disease combining knowledge graph and deep learning, IEEE Access, vol. 8, pp. 149787–149795, 2020.
Crossref Google Scholar
[93]
Y. Liu, H. Li, and Y. Chen, Using knowledge graph to handle label imperfection, in PAKDD 2014 Int. Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA on Trends and Applications in Knowledge Discovery and Data Mining, Tainan, China, 2014, pp. 345–356.
Google Scholar
[94]
Q. Zhu, D. T. Nguyen, I. Grishagin, N. Southall, E. Sid, and A. Pariser, An integrative knowledge graph for rare diseases, derived from the Genetic and Rare Diseases Information Center (GARD), J. Biomed. Semant., vol. 11, no. 1, p. 13, 2020.
Crossref Google Scholar
[95]
T. Liu, X. Pan, X. Wang, K. A. Feenstra, J. Heringa, and Z. Huang, Predicting the relationships between gut microbiota and mental disorders with knowledge graphs, Health Inf. Sci. Syst., vol. 9, no. 1, p. 3, 2021.
Crossref Google Scholar
[96]
H. Gao, Y. Ni, X. Mo, D. Li, S. Teng, Q. Huang, S. Huang, G. Liu, S. Zhang, Y. Tang, et al., Drug repositioning based on network-specific core genes identifies potential drugs for the treatment of autism spectrum disorder in children, Comput. Struct. Biotechnol. J., vol. 19, pp. 3908–3921, 2021.
Crossref Google Scholar
[97]
W. Li, L. Chai, C. Yang, and X. Wang, An evolutionary analysis of DBpedia datasets, in 15th Int. Conf. on Web Information Systems and Applications, Taiyuan, China, 2018, pp. 317–329.
Crossref Google Scholar
[98]
N. Li, Z. Yang, L. Luo, L. Wang, Y. Zhang, H. Lin, and J. Wang, KGHC: A knowledge graph for hepatocellular carcinoma, BMC Med. Inform. Decis. Mak., vol. 20, no. 3, p. 135, 2020.
Crossref Google Scholar
[99]
R. Zhang, D. Hristovski, D. Schutte, A. Kastrin, M. Fiszman, and H. Kilicoglu, Drug repurposing for COVID-19 via knowledge graph completion, J. Biomed. Inform., vol. 115, p. 103696, 2021.
Crossref Google Scholar
[100]
Q. Wang, T. Wang, and C. Xu, Using a knowledge graph for hypernymy detection between Chinese symptoms, in 2018 Tenth Int. Conf. Advanced Computational Intelligence (ICACI), Xiamen, China, 2018, pp. 601–606.
Crossref Google Scholar
[101]
S. Ganesh and A. K. Talukder, Formal methods, artificial intelligence, big-data analytics, and knowledge engineering in medical care to reduce disease burden and health disparities, in 6th Int. Conf. on Big Data Analytics, Warangal, 2018, pp. 307–321.
Crossref Google Scholar
[102]
S. K. Mohamed, V. Nováček, and A. Nounu, Discovering protein drug targets using knowledge graph embeddings, Bioinformatics, vol. 36, no. 2, pp. 603–610, 2020.
Crossref Google Scholar
[103]
K. Lei, K. Yuan, Q. Zhang, and Y. Shen, MedSim: A novel semantic similarity measure in bio-medical knowledge graphs, in 11th Int. Conf. on Knowledge Science, Engineering and Management, Changchun, China, 2018, pp. 479–490.
Crossref Google Scholar
[104]
V. K. C. Yan, X. Li, X. Ye, M. Ou, R. Luo, Q. Zhang, B. Tang, B. J. Cowling, I. Hung, C. W. Siu, et al., Drug repurposing for the treatment of COVID-19: A knowledge graph approach, Adv. Ther., vol. 4, no. 7, p. 2100055, 2021.
Crossref Google Scholar
[105]
Y. Pan, X. Lei, and Y. Zhang, Association predictions of genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, radiomics, drug, symptoms, environment factor, and disease networks: A comprehensive approach, Med. Res. Rev., vol. 42, no. 1, pp. 441–461, 2022.
Crossref Google Scholar
[106]
M. Kuhn, I. Letunic, L. J. Jensen, and P. Bork, The SIDER database of drugs and side effects, Nucl. Acids Res., vol. 44, no. D1, pp. D1075–D1079, 2016.
Crossref Google Scholar
[107]
A. L. Mitchell, T. K. Attwood, P. C. Babbitt, M. Blum, P. Bork, A. Bridge, S. D. Brown, H. Y. Chang, S. El-Gebali, M. I. Fraser, et al., InterPro in 2019: Improving coverage, classification and access to protein sequence annotations, Nucl. Acids Res., vol. 47, no. D1, pp. D351–D360, 2019.
Crossref Google Scholar
[108]
The UniProt Consortium, UniProt: The universal protein knowledgebase, Nucl. Acids Res., vol. 45, no. D1, pp. D158–D169, 2017.
Crossref Google Scholar
[109]
R. T. Sousa, S. Silva, and C. Pesquita, Evolving knowledge graph similarity for supervised learning in complex biomedical domains, BMC Bioinform., vol. 21, no. 1, p. 6, 2020.
Crossref Google Scholar
[110]
Y. Yang, Z. Huang, Y. Han, X. Hua, and W. Tang, Using knowledge graph for analysis of neglected influencing factors of statin-induced myopathy, in Int. Conf. on Brain Informatics, Beijing, China, 2017, pp. 304–311.
Crossref Google Scholar
[111]
B. Xu, Y. Xu, J. Liang, C. Xie, B. Liang, W. Cui, and Y. Xiao, CN-DBpedia: A never-ending Chinese knowledge extraction system, in 30th Int. Conf. on Industrial Engineering and Other Applications of Applied Intelligent Systems on Advances in Artificial Intelligence: From Theory to Practice, Arras, France, 2017, pp. 428–438.
Crossref Google Scholar
[112]
F. Li, Y. Jin, W. Liu, B. P. S. Rawat, P. Cai, and H. Yu, Fine-tuning bidirectional encoder representations from transformers (BERT)-based models on large-scale electronic health record notes: An empirical study, JMIR Med. Inform., vol. 7, no. 3, p. e14830, 2019.
Crossref Google Scholar
[113]
E. Tutubalina, Z. Miftahutdinov, S. Nikolenko, and V. Malykh, Medical concept normalization in social media posts with recurrent neural networks, J. Biomed. Inform., vol. 84, pp. 93–102, 2018.
Crossref Google Scholar
[114]
Y. F. Luo, W. Sun, and A. Rumshisky, MCN: A comprehensive corpus for medical concept normalization, J. Biomed. Inform., vol. 92, p. 103132, 2019.
Crossref Google Scholar
[115]
H. L. Nguyen, D. T. Vu, and J. J. Jung, Knowledge graph fusion for smart systems: A Survey, Inform. Fusion, vol. 61, pp. 56–70, 2020.
Crossref Google Scholar
[116]
Z. Lei, Y. Sun, Y. A. Nanehkaran, S. Yang, M. S. Islam, H. Lei, and D. Zhang, A novel data-driven robust framework based on machine learning and knowledge graph for disease classification, Future Gener. Comp. Syst., vol. 102, pp. 534–548, 2020.
Crossref Google Scholar
[117]
Y. Chen, T. Ma, X. Yang, J. Wang, B. Song, and X. Zeng, MUFFIN: Multi-scale feature fusion for drug-drug interaction prediction, Bioinformatics, vol. 37, no. 17, pp. 2651–2658, 2021.
Crossref Google Scholar
[118]
Q. Wang, Y. Ji, Y. Hao, and J. Cao, GRL: Knowledge graph completion with GAN-based reinforcement learning, Knowl-Based. Syst., vol. 209, p. 106421, 2020.
Crossref Google Scholar
[119]
R. Zhang, Y. Mao, and W. Zhao, Knowledge graphs completion via probabilistic reasoning, Inform. Sci., vol. 521, pp. 144–159, 2020.
Crossref Google Scholar
[120]
X. Chen, S. Jia, and Y. Xiang, A review: Knowledge reasoning over knowledge graph, Expert Syst. Appl., vol. 141, p. 112948, 2020.
Crossref Google Scholar
[121]
J. Chu, J. Chen, X. Chen, W. Dong, J. Shi, and Z. Huang, Knowledge-aware multi-center clinical dataset adaptation: Problem, method, and application, J. Biomed. Inform., vol. 115, p. 103710, 2021.
Crossref Google Scholar
[122]
D. Yue, K. Zhang, L. Zhuang, X. Zhao, O. Byambasuren, and H. Zan, Annotation scheme and specification for named entities and relations on Chinese medical knowledge graph, in 20th Workshop on Chinese Lexical Semantics, Beijing, China, 2020, pp. 563–574.
Crossref Google Scholar
[123]
N. Pattisapu, V. Anand, S. Patil, G. Palshikar, and V. Varma, Distant supervision for medical concept normalization, J. Biomed. Inform., vol. 109, p. 103522, 2020.
Crossref Google Scholar
[124]
S. Yin, D. Chen, and J. Le, Deep neural network based on translation model for diabetes knowledge graph, in 2017 5th Int. Conf. Advanced Cloud and Big Data, Shanghai, China, 2017, pp. 318–323.
Crossref Google Scholar
[125]
S. Biswas, P. Mitra, and K. S. Rao, Relation prediction of Co-morbid diseases using knowledge graph completion, IEEE/ACM Trans. Comput. Biol. Bioinform., vol. 18, no. 2, pp. 708–717, 2019.
Crossref Google Scholar
[126]
J. Santisteban and J. L. Tejada Carcamo, Unilateral Jaccard similarity coefficient, in SIGIR Workshop on Graph Search and Beyond’15, Santiago, Chile, 2015, pp. 23–27.
Google Scholar
[127]
M. Sheng, H. Zhang, Y. Zhang, C. Li, C. Xing, J. Wang, Y. Shao, and F. Gao, CLMed: A cross-lingual knowledge graph framework for cardiovascular diseases, in 16th Int. Conf. on Web Information Systems and Applications, Qingdao, China, 2019, pp. 512–517.
Crossref Google Scholar
[128]
M. Sheng, J. Wang, Y. Zhang, X. Li, C. Li, C. Xing, Q. Li, Y. Shao, and H. Zhang, DocKG: A knowledge graph framework for health with doctor-in-the-loopp, in 8th Int. Conf. on Health Information Science, Xi’an, China, 2019, pp. 3–14.
Crossref Google Scholar
[129]
J. T. Reese, D. Unni, T. J. Callahan, L. Cappelletti, V. Ravanmehr, S. Carbon, K. A. Shefchek, B. M. Good, J. P. Balhoff, T. Fontana, et al., KG-COVID-19: A framework to produce customized knowledge graphs for COVID-19 response, Patterns, vol. 2, no. 1, p. 100155, 2021.
Crossref Google Scholar
[130]
X. Xiu, Q. Qian, and S. Wu, Construction of a digestive system tumor knowledge graph based on Chinese electronic medical records: Development and usability study, JMIR Med. Inform., vol. 8, no. 10, p. e18287, 2020.
Crossref Google Scholar
[131]
B. M. J. Some, G. Bordea G, F. Thiessard, S. Schulz, and G. Diallo, Design considerations for a knowledge graph: The WATRIMed use case, Stud. Health Technol. Inform., vol. 259, pp. 59–64, 2019.
Google Scholar
[132]
S. Zheng, J. Rao, Y. Song, J. Zhang, X. Xiao, E. F. Fang, Y. Yang, and Z. Niu, PharmKG: A dedicated knowledge graph benchmark for bomedical data mining, Brief. Bioinform., vol. 22, no. 4, p. bbaa344, 2021.
Crossref Google Scholar
[133]
Y. Zhou, T. Zhou, T. Zhou, H. Fu, J. Liu, and L. Shao, Contrast-attentive thoracic disease recognition with dual-weighting graph reasoning, IEEE Trans. Med. Imaging., vol. 40, no. 4, pp. 1196–1206, 2021.
Crossref Google Scholar
[134]
X. Sun, Y. Man, Y. Zhao, J. He, and N. Liu, Incorporating description embeddings into medical knowledge graphs representation learning, in 4th Int. Conf. on Human Centered Computing, Mérida, Mexico, 2019, pp. 188–194.
Crossref Google Scholar
[135]
Q. Li, L. Li, J. Zhong, and L. F. Huang, Real-time sepsis severity prediction on knowledge graph deep learning networks for the intensive care unit, J. Vis. Commun. Image Represent., vol. 72, p. 102901, 2020.
Crossref Google Scholar
[136]
J. Jiang, H. Wang, J. Xie, X. Guo, Y. Guan, and Q. Yu, Medical knowledge embedding based on recursive neural network for multi-disease diagnosis, Artif. Intell. Med., vol. 103, p. 101772, 2020.
Crossref Google Scholar
[137]
X. Wang, Y. Zhang, X. Wang, and J. Chen, A knowledge graph enhanced topic modeling approach for herb recommendation, in 24th Int. Conf. on Database Systems for Advanced Applications, Chiang Mai, Thailand, 2019, pp. 709–724.
Crossref Google Scholar
[138]
W. J. Vlietstra, R. Vos, A. M. Sijbers, E. M. van Mulligen, and J. A. Kors, Using predicate and provenance information from a knowledge graph for drug efficacy screening, J. Biomed. Semant., vol. 9, no. 1, p. 23, 2018.
Crossref Google Scholar
[139]
C. Moon, C. Jin, X. Dong, S. Abrar, W. Zheng, R. Y. Chirkova, and A. Tropsha, Learning Drug-Disease-Target Embedding (DDTE) from knowledge graphs to inform drug repurposing hypotheses, J. Biomed. Inform., vol. 119, p. 103838, 2021.
Crossref Google Scholar
[140]
Y. Shen, K. Yuan, J. Dai, B. Tang, M. Yang, and K. Lei, KGDDS: A system for drug-drug similarity measure in therapeutic substitution based on knowledge graph curation, J. Med. Syst., vol. 43, no. 4, p. 92, 2019.
Crossref Google Scholar
[141]
X. Liang, D. Li, M. Song, A. Madden, Y. Ding, and Y. Bu, Predicting biomedical relationships using the knowledge and graph embedding cascade model, PLoS One, vol. 14, no. 6, p. e0218264, 2019.
Crossref Google Scholar
[142]
T. Qi, S. Qiu, X. Shen, H. Chen, S. Yang, H. Wen, Y. Zhang, Y. Wu, and Y. Huang, KeMRE: Knowledge-enhanced medical relation extraction for Chinese medicine instructions, J. Biomed. Inform., vol. 120, p. 103834, 2021.
Crossref Google Scholar
[143]
Y. Ge, T. Tian, S. Huang, F. Wan, J. Li, S. Li, X. Wang, H. Yang, L. Hong, N. Wu, et al., An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19, Signal Transduct. Target. Ther., vol. 6, no. 1, p. 165, 2021.
Crossref Google Scholar
[144]
J. Al-Saleem, R. Granet, S. Ramakrishnan, N. A. Ciancetta, C. Saveson, C. Gessner, and Q. Q. Zhou, Knowledge graph-based approaches to drug repurposing for COVID-19, J. Chem. Inf. Model., vol. 61, no. 8, pp. 4058–4067, 2021.
Crossref Google Scholar
[145]
Y. Yu, M. Li, L. Liu, Y. Li, and J. Wang, Clinical big data and deep learning: Applications, challenges, and future outlooks, Big Data Mining and Analytics, vol. 2, no. 4, pp. 288–305, 2019.
Crossref Google Scholar
Big Data Mining and Analytics
Volume 6 Issue 2,
June 2023
Pages 201-217
DOI: 10.26599/BDMA.2022.9020021
Cite this article:
Wu X, Duan J, Pan Y, et al. Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications. Big Data Mining and Analytics, 2023, 6(2): 201-217. https://doi.org/10.26599/BDMA.2022.9020021
Metrics & Citations  
Article History
Copyright
Rights and Permissions
Return

10.26599/BDMA.2022.9020021.T001Data sources of MKG and related research.

Source typeSourceRelated research
Real-word dataEMR[21,30,47,48,55,58,59,60,61,62,63,64,65,66,67,68,69,70,71]
EHR[23,25,72,73,74,75,76]
Chinese clinical dataset Levis hypertension[77]
Cancer registry data[15]
Clinically pediatric cases[33]
Clinical trial reports[13,78,79]
NHANES data set[14]
Scientific publicationsLiterature[4,10,13,25,27,37,46,49,50,58,78,79,80,81,82,83,84,85,86,87,88,89,90,91]
Textbooks[4,29,33,45]
Internet resource[6,21,29,41,45,50,79,85,87,92,93]
Guidelines[13,43,45,78,79]
Standard libraryMesh[16,29,46,50,58,79,85,90,94,95,96]
UMLs[7,18,31,46,49,50,51,58,59,64,71,79,81,82,83,86,94,95,96,97,98,99]
ICD9/10[45,46,48,55,59,66,67,70,71,72,74,85]
SNOMED-CT[13,17,70,71,74,79,85,100,101]
Open and shared medical knowledge databaseFreeBase[52,55]
SemMedDB[7,54,86,87,98,99]
RepoDB[53,56]
ROBOKOP[27,57]
DrugBank[13,19,48,49,66,67,79,102,103,104,105]
KEGG[20,49,95,102,105]
Google health knowledge graph[59]
SIDER[13,49,79,103,106]
Cancer/tumor/case dataset[15]
InterPro[102,107]
UniProt[87,102,104,108]
Gene Ontology[18,109]
OpenKG.CN[26,104]
Linked open data[110]
Linked life data[110]
Therapeutic target database[88,104]
BioGRID[104]
DBpedia/CN-DBpedia[34,97,111]

10.26599/BDMA.2022.9020021.T002Subjects of MKGs from studies.

SubjectsMKGScale of MKGs
# entity types# relation types# entities# facts
Integrative orientedKnowlife KG[50]71350 000609 322
MKG[47]9-22 508579 094
HG[31]–14400 000200 000
SMR KG[66]44367 2011 707 609
Disease orientedCLKG[127]––200 0001 000 000
Geriatric KG[26]67––
Depression KG[79]–––8 892 722
Knee osteoarthritis KG[65]810251829 972
SemKG[92]54––
DSTKG[130]716986811 005
Cancer KG[15]–––90 673 527
KGHC[98]1022502813 296
Rare disease KG[94]1042-3 819 62384 223 681
COVID-KOP[27]––45 3005 532 000
DRKF KG[49]–4312 497165 901
LT-D DB[51]311––
KDKG [13]–––10 146 311
StrokeKG[91]93046 000157 000
KGPA[71]1110––
Drug orientedWATRIMed KG[131]47275––
Drug KG[6]43582870 382
ADRs KG[28]4312 473154 239
KEGG_MED[102]21522912 112
TBKG[37]46––
MCKG[96]268014123 890
TCM-KG[60] [4]1275810 0001 000 000
Department orientedObstetric-KG[29]4226253863
PMKG[85]––22 02323 434
Biomedical orientedPharmKG[132]329–50 000
Bio KG[81]33682716 912
ROBOKOP KG[57]431579 399 969254 940 828
Integrated KG[82]–1713 527 42368 413 238
BioKGLM[87]511502 10096 500 000
OthersEMKN[30]53––
EBDPKG [72]––198910 380
EMR-based KG[58]46634 00014 000 000 000
SHKG[74]8328 5185591
Symptom KG[100]3250801521
PDD[48]3358 0304 244 856
DeepPS KG[67]7742 6131 888 950
G_Coder KG[55]69156020 000
FWA-KG[45]7–1 616 5495 963 444

10.26599/BDMA.2022.9020021.T003Intelligent applications based on MKG and RIP.

ScenarioApplicationRelated MKGRelated RIP
Intelligent retrievalCOVID-19 related literature retrieval[32]CORD-19 NEKG, CORD-19 AKGSP: query: retrieve
TCM retrieval[4]TCM KGSP: query: retrieve
Medical text retrieval[31]HGKRL: KGE: retrieve
Disease diagnosisPediatric disease prediction[33]Hybrid-KGGM: HKDP: classification
Geriatric disease reasoning[26]Geriatric KGSP: logic&rule inference: relation prediction
Rare disease diagnosis[34]–SP: entity feature → SVM: classification
Thyroid disease diagnosis[92]SemKGKRL: KGE → BLSTM: Classification
Predicate type 2 diabetes (T2D)[63]T2D KGGM: logistic regression → graph weighting: relation prediction & visualization
Predicate the severity of sepsis[135]–KRL: self-attentation → BiLSTM →  KGE → attentation: classification
Identify potential migraine biomarkers[82]–SP: extract compounds → filter&rank: ranking
Multi-disease diagnosis[136]CEMRs KGKRL: RNKN: classification
Decision support for UTI[3]–SP: EYE(BBNs&FCMs): ranking
Provide personalized disease ranking[51]LT-D DBGM: CG-Engine: ranking
Predicate subarachnoid hemorrhage[23]–SP: rule&logic: relation prediction
Clinical educationPredict outcomes to questions on the SIDES platform[35]OntoSIDESKRL: KGE: ranking
Disease diagnosis in a medical training system[36]–KRL: KGE: classification
Drug analysisPrediction of unknown ADRs[6]ADRsGM: graph weighting → machine learning algorithm: classification
ADR discovery[37]TBKGGM: graph weighting → Navie Bayes: relation prediction
Prediction of ADRs[28]ADRs KGKRL: Word2Vec → logistic regression: classification
Therapeutic substitution of antibiotics[103]DrugBankKRL: MedSim: similarity analysis
Combined drug therapies[78]–SP: rules → filter → automated algorithm: relation prediction
Herb recommendation[137]TCM KGKRL: KGETM&HC-KGETM: entity prediction
Prediction of DDIs[38]DrugBankGM: similarity measures → logistic regression: relation prediction
Drug-drug interaction prediction[8]DeepDDI, DecagonKRL: AAEs: relation prediction & classification
Analysis of neglected influencing factors of statin-induced myopathy[110]LOD, LLDSP: SPARQL query: other analysis
Drug discovery[80]SemKGGM: path exploration → logistic regression: ranking
Drug efficacy screening[138]Guney, EMCGM: path exploration → random forest: classification
Drug repurposing[139]DTINetKRL: DDTE: entity prediction
Drug repurposing[56]RepoDBGM: extract paths → random forest: classification
Drug repurposing against Parkinson’s disease[49]DRKF KGKRL: KGE → SVM: classification
Drug repurposing for COVID-19[90]SemMedDBKRL: KGE: entity prediction
Drug-drug similarity(DDS)[140]KGDDS KGKRL: KGE → similarity compute: similarity analysis
Safe medicine recommendation[66]SMR KGKRL: KGE: ranking
Prediction of drug target proteins[102]KGEE_MEDKRL: KGE (TriModel) →relation prediction
CDSSA data-intensive CDSS platform[39]IDS KGSP: rule inference: other analysis
Intelligent diagnose assistant system[74]SHKGSP: semantic inference: other analysis
Medical aided diagnosis system[40]–KRL: → KGE (TransR) → LSTM: other analysis
Medical Q&A and chatbotMedical question answering[58]–SP: probabilistic inference: ranking
An online medical chatbot system[41]–SP: extract entity → query: retrieve
A QA system for smart health[64]DSQA KGSP: generate QA pairs → query: retrieve
Medical question answering system[42]–SP: query: retrieve
Intelligent nursingEldercare[43]–SP: query: other analysis
Balance reactive care and proactive care[72]EBDPKGSP: Bayesian: classification
Health managementDietary supplements (DSs)[44]iDISKSP: Web App → query: visualization& KBQA
Health risk prediction[14]HKGGM: optimisation algorithm: classification
Predicting the status of health risks[46]KB-HIGKRL: graph weighting → Word2Vec: classification
Estimating personalized risk ranking[51]LT-D DBKRL: CG-Engine: ranking
OthersFraud, waste, and abuse (FWA) detection[45]FWA KGSP: rule inference: relation prediction
Deep patient similarity[67]DeepPS KGKRL: KGE → CNN: similarity
Automated ICD coding[55]G_Coder KGKRL: SDNE → attentation → FGM: classification
Semantic enhancement[11,87,141,142]––