Overview of the application of knowledge graphs in the medical field_Journal of Biomedical Engineering

Authors：

WANG Caiyun ¹ , ZHENG Zengliang ¹ , CAI Xiaoqiong ¹ , HUANG Jihan ² ,  SU Qianmin ¹

1. College of Electrical and Electronic Engineering, Shanghai University Of Engineering Science, Shanghai 201620, P. R. China;
2. Center for Drug Clinical Research, Shanghai University of Chinese Medicine, shanghai 201203, P. R. China;

Corresponding author：

Keywords：

Assistant diagnosis; Intelligent prediction; Medical knowledge graph; Wise medical

DOI：

10.7507/1001-5515.202204016

Video：

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With the booming development of medical information technology and computer science, the medical services industry is gradually transiting from information technology to intelligence. The medical knowledge graph plays an important role in intelligent medical applications such as knowledge questions and answers and intelligent diagnosis, and is a key technology for promoting wise medical care and the basis for intelligent management of medical information. In order to fully exploit the great potential of knowledge graphs in the medical field, this paper focuses on five aspects: inter-drug relationship discovery, assisted diagnosis, personalized recommendation, decision support and intelligent prediction. The latest research progress on medical knowledge graphs is introduced, and relevant suggestions are made in light of the current challenges and problems faced by medical knowledge graphs to provide reference for promoting the wide application of medical knowledge graphs.

Citation： WANG Caiyun, ZHENG Zengliang, CAI Xiaoqiong, HUANG Jihan, SU Qianmin. Overview of the application of knowledge graphs in the medical field. Journal of Biomedical Engineering, 2023, 40(5): 1040-1044. doi: 10.7507/1001-5515.202204016 Copy

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7.	Korn D, Bobrowski T, Li M, et al. COVID-KOP: integrating emerging COVID-19 data with the ROBOKOP database. Bioinformatics, 2021, 37(4): 586-587.
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13.	Bean D M, Wu H, Iqbal E, et al. Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records. Scientific Reports, 2017, 7(1): 16416.
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16.	Li Y, Zhang T, Yang Y, et al. Artificial intelligence-aided decision support in paediatrics clinical diagnosis: development and future prospects. Journal of International Medical Research, 2020, 48(9): 300060520945141.
17.	Chai X. Diagnosis method of thyroid disease combining knowledge graph and deep learning. IEEE Access, 2020, 8: 149787-149795.
18.	Xie Y, Hu L, Chen X, et al. Auxiliary diagnosis based on the knowledge graph of TCM syndrome. Computers Materials & Continua, 2020, 65(1): 481-494.
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27.	朱超宇, 刘雷. 基于知识图谱的医学决策支持应用综述. 数据分析与知识发现, 2020, 4(12): 26-32.
28.	Shang Y, Tian Y, Zhou M, et al. EHR-oriented knowledge graph system: toward efficient utilization of non-used information buried in routine clinical practice. IEEE Journal of Biomedical and Health Informatics, 2021, 25(7): 2463-2475.
29.	Wang L, Xie H, Han W, et al. Construction of a knowledge graph for diabetes complications from expert-reviewed clinical evidences. Computer Assisted Surgery, 2020, 25(1): 29-35.
30.	Xiong W, Cao J, Zhou X, et al. Design and evaluation of a prescription drug monitoring program for chinese patent medicine based on knowledge graph. Evidence-Based Complementary and Alternative Medicine, 2021, 2021: 9970063.
31.	Chu J, Chen J, Chen X, et al. Knowledge-aware multi-center clinical dataset adaptation: problem, method, and application. Journal of Biomedical Informatics, 2021, 115: 103710.
32.	Sun Z, Yin H, Chen H, et al. Disease prediction via graph neural networks. IEEE Journal of Biomedical and Health Informatics, 2020, 25(3): 818-826.
33.	Pham T, Tao X, Zhang J, et al. Graph-based multi-label disease prediction model learning from medical data and domain knowledge. Knowledge-Based Systems, 2022, 235: 107662.
34.	Wang S, Xu F, Li Y, et al. KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers. Bioinformatics, 2021, 37: i418-i425.
35.	Teng F, Yang W, Chen L, et al. Explainable prediction of medical codes with knowledge graphs. Frontiers in Bioengineering and Biotechnology, 2020, 8: 867.

1. Hogan A, Blomqvist E, Cochez M, et al. Knowledge graphs. Synthesis Lectures on Data, Semantics, and Knowledge, Springer, 2022.
2. Lin J, Zhao Y, Huang W, et al. Domain knowledge graph-based research progress of knowledge representation. Neural Computing and Applications, 2021, 33(2): 681-690.
3. Abu-Salih B. Domain-specific knowledge graphs: A survey. Journal of Network and Computer Applications, 2021, 185: 103076.
4. Yue D, Zhang K, Zhuang L, et al. Annotation scheme and specification for named entities and relations on Chinese medical knowledge graph//Chinese Lexical Semantics: 20th Workshop (CLSW), Beijing, 2019: 563–574.
5. Mohamed S K, Nováček V, Nounu A. Discovering protein drug targets using knowledge graph embeddings. Bioinformatics, 2020, 36(2): 603-610.
6. 桑盛田, 杨志豪, 刘晓霞, 等. 融合知识图谱与深度学习的药物发现方法. 模式识别与人工智能, 2018, 31(12): 1103-1110.
7. Korn D, Bobrowski T, Li M, et al. COVID-KOP: integrating emerging COVID-19 data with the ROBOKOP database. Bioinformatics, 2021, 37(4): 586-587.
8. Al-Saleem J, Granet R, Ramakrishnan S, et al. Knowledge graph-based approaches to drug repurposing for COVID-19. Journal of Chemical Information and Modeling, 2021, 61(8): 4058-4067.
9. Smith D P, Oechsle O, Rawling M J, et al. Expert-augmented computational drug repurposing identified baricitinib as a treatment for COVID-19. Frontiers in Pharmacology, 2021, 12: 709856.
10. Zhang R, Hristovski D, Schutte D, et al. Drug repurposing for COVID-19 via knowledge graph completion. Journal of Biomedical Informatics, 2021, 115: 103696.
11. Yan V K C, Li X, Ye X, et al. Drug repurposing for the treatment of COVID-19: a knowledge graph approach. Advanced Therapeutics, 2021, 4(7): 2100055.
12. MacLean F. Knowledge graphs and their applications in drug discovery. Expert Opinion on Drug Discovery, 2021, 16(9): 1057-1069.
13. Bean D M, Wu H, Iqbal E, et al. Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records. Scientific Reports, 2017, 7(1): 16416.
14. Güvenç Paltun B, Kaski S, Mamitsuka H. Machine learning approaches for drug combination therapies. Briefings in Bioinformatics, 2021, 22(6): bbab293.
15. Du J, Li X. A knowledge graph of combined drug therapies using semantic predications from biomedical literature: algorithm development. JMIR Medical Informatics, 2020, 8(4): e18323.
16. Li Y, Zhang T, Yang Y, et al. Artificial intelligence-aided decision support in paediatrics clinical diagnosis: development and future prospects. Journal of International Medical Research, 2020, 48(9): 300060520945141.
17. Chai X. Diagnosis method of thyroid disease combining knowledge graph and deep learning. IEEE Access, 2020, 8: 149787-149795.
18. Xie Y, Hu L, Chen X, et al. Auxiliary diagnosis based on the knowledge graph of TCM syndrome. Computers Materials & Continua, 2020, 65(1): 481-494.
19. Zhang D, Jia Q, Yang S, et al. Traditional Chinese medicine automated diagnosis based on knowledge graph reasoning. Computers Materials & Continua, 2022, 71(1): 159-170.
20. Song F, Wang B, Tang Y, et al. Research of medical aided diagnosis system based on temporal knowledge graph//International Conference on Advanced Data Mining and Applications. Springer, 2020: 236-250.
21. Wen H, Song J, Pan X. Physician recommendation on healthcare appointment platforms considering patient choice. IEEE Transactions on Automation Science and Engineering, 2019, 17(2): 886-899.
22. Liu D, Ma Z, Zhou Y, et al. Intelligent hospital guidance system based on multi-round conversation//2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019: 1540-1543.
23. Yang Y, Rao Y, Yu M, et al. Multi-layer information fusion based on graph convolutional network for knowledge-driven herb recommendation. Neural Networks, 2022, 146: 1-10.
24. Wang Y. A novel Chinese traditional medicine prescription recommendation system based on knowledge graph//Journal of Physics: Conference Series. IOP Publishing, 2020, 1487(1): 012019.
25. Gong F, Wang M, Wang H, et al. SMR: medical knowledge graph embedding for safe medicine recommendation. Big Data Research, 2021, 23: 100174.
26. Chi Y, Yu C, Qi X, et al. Knowledge management in healthcare sustainability: a smart healthy diet assistant in traditional Chinese medicine culture. Sustainability, 2018, 10(11): 4197.
27. 朱超宇, 刘雷. 基于知识图谱的医学决策支持应用综述. 数据分析与知识发现, 2020, 4(12): 26-32.
28. Shang Y, Tian Y, Zhou M, et al. EHR-oriented knowledge graph system: toward efficient utilization of non-used information buried in routine clinical practice. IEEE Journal of Biomedical and Health Informatics, 2021, 25(7): 2463-2475.
29. Wang L, Xie H, Han W, et al. Construction of a knowledge graph for diabetes complications from expert-reviewed clinical evidences. Computer Assisted Surgery, 2020, 25(1): 29-35.
30. Xiong W, Cao J, Zhou X, et al. Design and evaluation of a prescription drug monitoring program for chinese patent medicine based on knowledge graph. Evidence-Based Complementary and Alternative Medicine, 2021, 2021: 9970063.
31. Chu J, Chen J, Chen X, et al. Knowledge-aware multi-center clinical dataset adaptation: problem, method, and application. Journal of Biomedical Informatics, 2021, 115: 103710.
32. Sun Z, Yin H, Chen H, et al. Disease prediction via graph neural networks. IEEE Journal of Biomedical and Health Informatics, 2020, 25(3): 818-826.
33. Pham T, Tao X, Zhang J, et al. Graph-based multi-label disease prediction model learning from medical data and domain knowledge. Knowledge-Based Systems, 2022, 235: 107662.
34. Wang S, Xu F, Li Y, et al. KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers. Bioinformatics, 2021, 37: i418-i425.
35. Teng F, Yang W, Chen L, et al. Explainable prediction of medical codes with knowledge graphs. Frontiers in Bioengineering and Biotechnology, 2020, 8: 867.

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