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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 11

Review of medication recommendation method based on knowledge graph

Peng Lin
Wang Yu
Ye Qing
Cheng Chunlei
He Jia
School of Computer Science, Jiangxi University of Chinese Medicine, Jiangxi nanchang 330004, China
DOI: 10.19734/j.issn.1001-3695.2025.03.0094

Abstract

Medication recommendation utilizes an analysis of individual health status, medical history, genetic information, and lifestyle factors to provide personalized medication treatment plans for patients. However, this technology still faces challenges such as data sparsity, cold start problems, and interpretability in practical applications. The knowledge graph, with its rich structured semantic knowledge, serves as auxiliary information for recommendation systems, effectively addressing these issues and enhancing system performance. Therefore, this article reviews the current development status of medication recommendation methods based on knowledge graphs and their applications in various problems. Firstly, the article systematically outlines the relevant background knowledge, then identifies common and domain-specific issues in medication recommendation. The article discusses the advantages and limitations of medication recommendation methods based on knowledge graphs from both problem and technological perspectives, including traditional knowledge graph recommendation method, recommendation method for fusing multimodal knowledge graph, and recommendation method for knowledge graphs integrated with large language model. Finally, the article proposes insights into the future development prospects of this field.

Foundation Support

国家自然科学基金资助项目(82260988)
江西省自然科学基金资助项目(20224BAB206102)
江西省教育厅科学技术研究项目(GJJ2200923)
江西省卫生和计划生育委员会科技计划项目(202211404)
江西省高校人文社会科学研究项目(JC19125)
江西中医药大学2023年度重点学科建设经费资助课题(2023jzzdxk027)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.03.0094
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 11

Publish History

[2025-07-03] Accepted Paper

Cite This Article

彭琳, 汪宇, 叶青, 等. 基于知识图谱的药物推荐方法研究综述 [J]. 计算机应用研究, 2025, 42 (11). (2025-07-08). https://doi.org/10.19734/j.issn.1001-3695.2025.03.0094. (Peng Lin, Wang Yu, Ye Qing, et al. Review of medication recommendation method based on knowledge graph [J]. Application Research of Computers, 2025, 42 (11). (2025-07-08). https://doi.org/10.19734/j.issn.1001-3695.2025.03.0094. )

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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