A practical study on knowledge graph empowering medical basic education

  • Yu Li ,
  • Deng Weimin ,
  • Zhang Shijie ,
  • Mo Jing ,
  • Wen Ke ,
  • Yan Jingrui
Expand
  • 1Teaching and Research Section of Computer, School of Basic Medicine, Tianjin Medical University, Tianjin 300070, China;
    2Teaching Office, School of Basic Medicine, Tianjin Medical University, Tianjin 300070, China;
    3Department of Pharmacology, School of Basic Medicine, Tianjin Medical University, Tianjin 300070, China;
    4Teaching and Research Section of Pathology , School of Basic Medicine, Tianjin Medical University, Tianjin 300070, China

Received date: 2023-07-29

  Online published: 2024-07-02

Supported by

Industry-University Cooperation and Collaborative Education Project of Ministry of Education of China (220503924191616); Chinese Medical Association Medical Education Branch and China Association of Higher Education Medical Education Specialized Committee 2016 Medical Education Research Project (2020A-N06018); Program of Tianjin Undergraduate Teaching Quality and Teaching Reform (B231006204); Project of Education and Teaching Research of Tianjin Medical University (2023JXZD04)

Abstract

Objective To study the effectiveness of the self-developed knowledge graph software platform empowering basic medical teaching for clinical students. Methods Under the guidance of the subject teachers, the platform was opened as an assisted learning tool to 259 clinical students of Grade 2020 in Tianjin Medical University for 6 months, from January 2022 to July 2022. The students of Grade 2020 were used as the experimental group, and 248 students of Grade 2018 who were in the same major and had not accessed the platform ever were enrolled as the control group. Results of the Comprehensive Examination of Basic Medical Education Stage, organized by Tianjin Medical University, were compared. Mann-Whitney U test was used for statistical analysis. Results The total score of the students in experimental group [84.0(79.7, 88.0)] was significantly higher than that of the students in control group [69.0(59.5, 78.0)]; with respect to questions based on logical reasoning, the score of the students in the experimental group [67.6(55.5, 79.7)] was higher than that of the students in the control group [61.1(54.3, 73.6)]. All differences were statistically significant (all P<0.05). Conclusions Knowledge graph can significantly help students improve their learning effectiveness and enhance their logical reasoning ability.

Cite this article

Yu Li , Deng Weimin , Zhang Shijie , Mo Jing , Wen Ke , Yan Jingrui . A practical study on knowledge graph empowering medical basic education[J]. Chinese Journal of Medical Education, 2024 , 44(7) : 513 -516 . DOI: 10.3760/cma.j.cn115259-20230729-00055

References

[1] Pujara J, Miao H, Getoor L, et al. Knowledge graph identification// Semantic Web Science Association. Proceedings of the 12th international semantic web conference - part I[A],Berlin, 2013. Cham: Springer, 2013:542-557.
[2] Ehrlinger L, Wöß W. Towards a definition of knowledge graphs// Semantic Web Company. Joint proceedings of the posters and demos track of the 12th international conference on semantic systems - SEMANTiCS2016 and the 1st international workshop on semantic change & evolving semantics (SuCCESS'16) [A]. Leipzig: CEUR Workshop Proceedings, 2016: 1-4.
[3] Rotmensch M, Halpern Y, Tlimat A, et al. Learning a health knowledge graph from electronic medical records[J]. Sci Rep, 2017,7(1):1-11. DOI: 10.1038/s41598-017-05778-z.
[4] Lan Y, He S, Liu K, et al. Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion[J]. BMC Med Inform Decis Mak, 2021,21(Suppl 9):1-10. DOI: 10.1186/s12911-021-01622-7.
[5] 史宇坤, 许姝艺, 董少春. 基于知识图谱的增强型混合式学习的教学实践与思考[J].高校地质学报,2022,28(3):387-393. DOI: 10.16108/j.issn1006-7493.2021085.
[6] Cope B, Kalantzis M, Zhai C, et al. Bioinformational philosophy and postdigital knowledge ecologies[M]. Cham: Springer, 2022: 133-159.
[7] 施江勇,唐晋韬,王勇军,等.基于知识图谱的新兴领域课程教学资源建设[J].高等工程教育研究, 2022, 70(3):15-20.
[8] 戈其平, 钟艳如. 基于数学教学的知识图谱构建[J].计算机技术与发展,2019,29(3):187-189. DOI: 10.3969/j.issn.1673-629X.2019.03.039.
[9] 张春霞, 彭成, 罗妹秋, 等. 数学课程知识图谱构建及其推理[J].计算机科学,2020,47(z2):573-578. DOI: 10.11896/jsjkx.191200141.
[10] Wu X , Duan J , Pan Y ,et al. Medical knowledge graph: data sources, construction, reasoning, and applications[J].Big Data Mining and Analytics, 2023, 6(2):201-217. DOI:10.26599/BDMA.2022.9020021.
[11] Li Z, Zhang Y, Huang R, et al. Construction of depression knowledge graph based on biomedical literature//IEEE. Proceedings of IEEE international conference on bioinformatics and biomedicine[A], Houston, 2021. New York: IEEE, 2021: 1849-1855.
[12] 周倩, 魏涛, 胡紫宜, 等. 国内外临床教学中应用3D打印模型相关研究的知识图谱分析[J].中华医学教育杂志,2021,41(1):40-43. DOI: 10.3760/cma.j.cn115259-20200721-01092.
[13] 张靓, 周敏, 贺培凤, 等. 以知识图谱为特色的健康数据素养教育体系构建[J].中华医学教育探索杂志,2020,19(1):7-11. DOI: 10.3760/cma.j.issn.2095-1485.2020.01.002.
[14] 李艳君, 黄德生, 关鹏, 等. 虚拟仿真技术在我国医学教育领域相关研究中应用的科学知识图谱分析[J].中华医学教育杂志,2020,40(12):992-996. DOI: 10.3760/cma.j.cn115259-20200216-00138.
[15] 闫景瑞, 邓为民, 张士杰, 等. 基础医学教育阶段核心课程知识图谱的构建与应用[J].中华医学教育杂志,2024,44(3):176-179. DOI: 10.3760/cma.j.cn115259-20230301-00192.
Outlines

/