Research on the construction of a health technology assessment indicator knowledge base based on a multi-granularity knowledge representation model_Chinese Journal of Evidence-Based Medicine

Authors：

SHEN Liu ¹ , GUO Zhen ¹ ,  SUN Haixia ¹ , QIAN Qing ¹ , XIAO Yue ² , QIU Yingpeng ³ , LIN Yan ¹ , SHI Liwei ³ , CHEN Haoran ¹ , PU Siyue ¹ , ZHAO Yifan ⁴

1. Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, P. R. China;
2. National Center for Mental Health, Beijing 100032, P. R. China;
3. China National Health Development Research Center, Beijing 100044, P. R. China;
4. School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing 210009, P. R. China;

Corresponding author：

SUN Haixia, Email: sun.haixia@imicams.ac.cn

Keywords：

Health technology assessment (HTA); Knowledge representation model; Indicator knowledge base; Knowledge organization

DOI：

10.7507/1672-2531.202501140

Video：

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Objective To investigate the construction strategy of a knowledge base for health technology assessment (HTA) indicators based on a multi-granularity knowledge representation model, in order to meet the users' diverse demands for HTA knowledge services. Methods Firstly, we constructed a multi-granularity HTA indicator knowledge representation model based on systematically analyzing the content and structure of the HTA indicator system in literature. Secondly, we extracted multi-granularity HTA indicator knowledge from literatures and conduct subject indexing in a human-computer collaborative way. Finally, based on the HTA knowledge service requirements, a prototype of the HTA indicator knowledge base-HTA Indicators was designed and developed. Results A multi-granularity HTA indicator knowledge representation model was constructed, covering 5 core knowledge units(indicator systems, indicator items, formulas, measurement variables, and subjects), 20 types of attributes, and 12 types of relationships. This model represents the intrinsic characteristics and connections between multi-granularity indicator knowledge units. Knowledge extraction and subject indexing of multi-grain HTA indicators were conducted based on 227 HTA indicator documents, forming instance data. Finally, a prototype of the HTA indicator knowledge base, named HTA Indicators, was developed.HTA Indicators provides services such as multi-granularity HTA indicator knowledge retrieval, navigation, and linking. Conclusion The construction strategy of the HTA indicator knowledge base based on the multi-granularity knowledge representation model is feasible. The indicator knowledge base can achieve multi-dimensional semantic organization of indicator knowledge, provide multi-level and multi-dimensional indicator knowledge retrieval and discovery services, and meet the users' demand for precise HTA knowledge. In the future, we will explore the use of cutting-edge technologies such as large language models to achieve the automated construction of large-scale HTA knowledge, thereby enhancing the efficiency and intelligence level of knowledge base construction.

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1. 陈英耀, 赵琨. 卫生技术评估(第2版). 北京: 人民卫生出版社, 2023.
2. 吴雪, 景城阳, 施兰君, 等. 国内外卫生技术评估方法指南的系统评价. 中国循证医学杂志, 2024, 24(9): 1021-1029.
3. 李静. 卫生技术评估的基本方法. 中国循证医学杂志, 2003, (4): 315-320.
4. 施文凯, 吕兰婷. 基本医疗保险循证决策体系构建: 卫生技术评估的作用. 中国卫生政策研究, 2019, 12(11): 73-79.
5. 储呈晨, 金玮, 姜瑞瑶. 基于多准则决策分析的医疗影像设备动态采集技术评价体系研究. 中国医疗器械杂志, 2024, 48(6): 652-657.
6. 石晶金, 邱骏, 周蒨, 等. 价值医疗视角下妇产专科医院卫生技术评价指标体系的构建研究. 中国医院管理, 2023, 43(5): 23-26.
7. Zadeh L A. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst, 1997, 90(2): 111-127.
8. Bliss H E. The organization of knowledge and the system of the sciences. New York: Henry Holt and Company, 1929: 356-357.
9. 冯儒佳, 王忠义, 王艳凤, 等. 科技论文的多粒度知识组织框架研究. 情报科学, 2016, 34(12): 46-50,54.
10. 苏新宁, 等. 面向知识服务的知识组织理论与方法. 北京: 科学出版社, 2014.
11. 范昊, 王一帆. 知识关联视角下标准文档的多粒度知识组织方法研究. 信息资源管理学报, 2024, 14(4): 133-145.
12. Abecker A, van Elst L. Ontologies for knowledge management // Steffen Staab, Rudi Studer. Handbook on ontologies. 2nd ed. Heidelberg: Springer, 2009: 713-734.
13. 相雅凡, 刘东苏, 马续补, 等. 面向信息公开的突发公共卫生事件知识表示模型构建. 情报学报, 2024, 43(5): 538-552.
14. 王忠义, 郑鑫, 王珂莹. 面向用户生成内容的多粒度知识组织研究. 情报学报, 2022, 41(10): 1034-1043.
15. 赵冠壹, 韩松花. 科技文献的多粒度知识组织研究. 情报科学, 2023, 41(8): 134-138,161.
16. 索传军, 戎军涛. 知识元理论研究述评. 图书情报工作, 2021, 65(11): 133-142.
17. 刘蔓. 学报文献主题标引规范化探索与实践. 四川图书馆学报, 2015, (4): 39-42.
18. 沈柳, 孙海霞, 王嘉阳, 等. 心力衰竭知识图谱的构建. 中华医学图书情报杂志, 2019, 28(5): 1-5.
19. 赵军, 刘康, 何世柱, 等. 知识图谱. 北京: 高等教育出版社, 2018.
20. 戴泽琦, 景城阳, 吴雪, 等. 基于EVIDEM框架的中成药临床综合评价指标体系构建及解读. 中国实验方剂学杂志, 2024, 30(6): 134-143.
21. 文庭孝, 罗贤春, 刘晓英, 等. 知识单元研究述评. 中国图书馆学报, 2011, 37(5): 75-86.
22. 孙海霞, 郝洁, 郭臻, 等. 基于知识元的细粒度医学量表文档知识表示框架构建. 数字图书馆论坛, 2023, 19(12): 86-98.
23. 李丹亚, 李军莲. 医学知识组织系统: 术语与编码. 北京: 科学出版社, 2019.
24. National Library of Medicine. Medical Subject Headings.
25. Xu T, Gu Y, Xue M, et al. Knowledge graph construction for heart failure using large language models with prompt engineering. Front Comput Neurosci, 2024, 18(7): 1389475.
26. Cao L, Sun J, Cross A, et al. An automatic and end-to-end system for rare disease knowledge graph construction based on ontology-enhanced large language models: development study. JMIR Med Inform, 2024, 12(12): e60665.
27. Liu J, Chabot Y, Troncy R, et al. From tabular data to knowledge graphs: a survey of semantic table interpretation tasks and methods. J Web Semant, 2023, 76: 1-28.
28. Zhao S, Sun X. Enabling controllable table-to-text generation via prompting large language models with guided planning. Knowl-Based Syst, 2024, 304: 127678.
29. The International Network of Agencies for Health Technology Assessment. International HTA Database.
30. Bennett B, Coventry E, Greenway N, et al. The NICE process for developing quality standards and indicators. Z Evid Fortbild Qual Gesundhwes, 2014, 108(8-9): 481-486.

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