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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 8

Link prediction method of medical temporal knowledge graph based on evolutionary learning

Niu Chongqing
Lu Jing
Du Yuxuan
Wang Shaoyu
School of Optoelectronic Information & Computer Engineering, University of Shanghai for Science & Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2025.01.0007

Abstract

This study extracts medical entities and their relations from electronic health records, constructs a medical knowledge graph, and employs link prediction models to enhance the prediction performance of medical events such as disease progression, treatment plans, and drug responses, thereby assisting clinical decision-making. However, Current temporal link prediction models fail to fully utilize the structural dependencies between facts and struggle to effectively capture the dynamic evolution of entities and relationships. This paper proposes a medical temporal knowledge graph link prediction model called MedEvoLP (Evolutionary Learning for Medical Temporal Knowledge Graph Link Prediction) . The proposed approach constructs a relation co-occurrence graph and utilizes relation graph convolutional networks and temporal graph convolutional networks for recursive evolution to capture the structural dependencies between co-occurring medical facts fully utilize the rich contextual information in the medical knowledge graph. MedEvoLP also uses multiple adjacent timestamps to form a timestamp sequence as a time unit to participate in the dynamic evolution of patient entity embedding and relation embedding, effectively capturing the dynamic evolution characteristics. This paper conducts comparative experiments on clinical records extracted from the MIMIC-III and DiabetesP datasets against eight benchmark models. The results demonstrate that MedEvoLP outperforms existing baseline models on multiple evaluation metrics. For example, on the MIMIC-III dataset, MedEvoLP achieved the Hits@1 rate of 42.37%, proving MedEvoLP demonstrates superior performance.

Foundation Support

国家自然科学基金资助项目(61703278)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.01.0007
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 8

Publish History

[2025-03-28] Accepted Paper

Cite This Article

牛崇庆, 卢菁, 杜钰萱, 等. 基于演化学习的医疗时序知识图谱链接预测方法 [J]. 计算机应用研究, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2025.01.0007. (Niu Chongqing, Lu Jing, Du Yuxuan, et al. Link prediction method of medical temporal knowledge graph based on evolutionary learning [J]. Application Research of Computers, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2025.01.0007. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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