知识图谱在核应急医学救援领域中的应用初探

摘要/Abstract

摘要： 知识图谱作为一种新型的知识组织形式,推动了核与辐射领域应急医学救援的效率提升、准确化和智能化的发展。本文主要介绍知识图谱的概念、构建核应急医学救援领域知识图谱的关键技术,分析了其在核应急医学救援领域中的应用,旨在为进一步提升知识图谱在该领域的应用提供参考。

关键词: 核应急, 医学救援, 知识图谱, 人工智能

Abstract: As a novel form of knowledge organization, knowledge graphs have enhanced the efficiency, accuracy, and intelligence of emergency medical rescue in the nuclear and radiation field. This paper introduces the concept of knowledge graphs, the key technologies for constructing knowledge graphs in the field of nuclear emergency medical rescue, and analyzes their applications in this field, aiming to provide references for further enhancing the application of knowledge graphs in this area.

Key words: nuclear emergency, medical rescue, knowledge graph, artificial intelligence

中图分类号:

郭姗姗, 侯备, 高燕, 朱亚男, 梁应凤, 刘柳. 知识图谱在核应急医学救援领域中的应用初探[J]. 辐射防护通讯, 2026, 46(2): 44-48.

GUO Shanshan, HOU Bei, GAO Yan, ZHU Yanan, LIANG Yingfeng, LIU Liu. Research on the application of knowledge graphs in nuclear emergency medicalrescue field[J]. RADIATION PROTECTION BULLETIN, 2026, 46(2): 44-48.

参考文献 32

[1]	Dainiak N, Delli C D, Bohan M, et al. Literature review and global consensus on medical preparedness for a nuclear or radiological emergency[J]. Health Physics, 2020, 118(6): 623-640.
[2]	RUAN Fang, CHEN Chunhua, CHENG Yuan, et al. Study on evaluation method for nuclear emergency rescue measures at containment vessel[J]. Annals of Nuclear Energy, 2021, 151: 107 942.
[3]	Alberts I L, Mercolli L, Pyka T, et al. Large language models (LLM) and ChatGPT: what will the impact on nuclear medicine be?[J]. European Journal of Nuclear Medicine and Molecular Imaging, 2023, 50(6): 1 549-1 552.
[4]	颜钰, 龚姝, 段棣飞, 等. 知识图谱在慢性病患者饮食管理中的应用进展[J]. 中华护理杂志, 2024, 59(6): 753-757.
[5]	孙秀英, 张晓丹. 一种面向工业物联网的知识图谱认知制造模型[J]. 计算机应用与软件, 2025, 42(5): 43-49+94.
[6]	Abu-Salih B, Alotaibi S. A systematic literature review of knowledge graph construction and application in education[J]. Heliyon, 2024, 10(3): e25383.
[7]	TIAN Yongli, JIN Yuchang, ZHAO Yadi, et al. Analysis of knowledge graph: hotspots and future trends in environmental education research[J]. Sustainability, 2024, 16(6): 2378.
[8]	Singhal A. Introducing the knowledge graph: things, not strings[EB/OL]. (2012-05-16)[2025-01-23]. https://blog.google/products/search/introducing-knowledge-graph-things-not/.
[9]	Hogan A, Blomqvist E, Cochez M, et al. Knowledge graphs[J]. ACM Computing Surveys (CSUR), 2022, 54(4): 1-37.
[10]	LIU Peifeng, QIAN Lu, ZHAO Xingwei, et al. Joint knowledge graph and large language model for fault diagnosis and its application in aviation assembly[J]. IEEE Transactions on Industrial Informatics, 2024, 20(6): 8 160-8 169.
[11]	郑鑫彤, 李燕, 庞建美, 等. 知识图谱在护理领域应用的研究进展[J]. 中国护理管理, 2024, 24(12): 1 910-1 913.
[12]	蒋川宇, 韩翔宇, 杨文蕊, 等. 医学知识图谱研究与应用综述[J]. 计算机科学, 2023, 50(3): 83-93.
[13]	XIONG Liping, ZENG Qiqiao, DENG Wuhong, et al. Precision nursing research based on multimodal knowledge graph[EB/OL]. (2023-12-25)[2024-01-04]. https://www.researchsquare.com/article/rs-3629829/v1.
[14]	Nadeem M, Fathi M. Comparing knowledge source integration methods for optimizing healthcare knowledge fusion in rescue operation[C]//2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS). St. Louis: IEEE, 2024: 1-7.
[15]	陈有宁. 核污染医学应急知识图谱技术研究[D]. 天津: 天津科技大学, 2024.
[16]	Epstein D, Radimislensky I, Lipsky A M, et al. Assessing the evolution of pre-hospital combat casualty care: a comparative study of two conflicts a decade apart[J]. The American Journal of Emergency Medicine, 2025, 88: 96-104.
[17]	Pamplin J C, Remondelli M H, Thota D, et al. Revolutionizing combat casualty care: the power of digital twins in optimizing casualty care through passive data collection[J]. Military Medicine, 2025, 190(1/2): 27-32.
[18]	Núñez-Chongo O, Asorey H, Rubio-Montero A J, et al. Convergent data-driven workflows for open radiation calculations: an exportable methodology to any field[J]. The Journal of Supercomputing, 2025, 81(3): 465.
[19]	The institute for Experiential AI Northeastern University. Northeastern University helps shape the future of emergency preparedness with AI and simulation gaming[EB/OL]. (2025-02-24)[2025-07-23]. https://ai.northeastern.edu/news/northeastern-university-helps-shape-the-future-of-emergency-preparedness-with-ai-and-simulation-gaming.
[20]	De Lorenzis f, Anfuso S, Migliorini M, et al. An improved virtual reality platform for training CBRN operators in reconnaissance procedures[C]//16th International Conference on Education and New Learning Technologies. Palma: IATED, 2024: 6 550-6 559.
[21]	国务院应急管理办公室. 2025年国家核和辐射卫生应急队伍培训演练圆满完成[EB/OL]. (2025-09-26)[2025-09-26]. https://www.nirp.cn/zhxx/202509/t20250926_312710.html.
[22]	ZHAO Xin, LI Xiaobo. Comparison of standard training to virtual reality training in nuclear radiation emergency medical rescue education[J]. Disaster Medicine and Public Health Preparedness, 2022, 17: e197.
[23]	Arhat H, Alinier G, Chaabna K, et al. Preparedness and emergency response strategies for chemical, biological, radiological and nuclear emergencies in disaster management: a qualitative systematic review[J]. Journal of Contingencies and Crisis Management, 2024, 32(3): e12592.
[24]	CHEN Jiahui, GE Xingtong, LI Weichao, et al. Construction of spatiotemporal knowledge graph for emergency decision making[C]//2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. Brussels: IEEE, 2021: 3 920-3 923.
[25]	LIU Shuai, HUANG Meng, YANG Guang, et al. Multimodal deep learning for entity relation extraction and spatiotemporal decision knowledge graph construction in earthquake emergency[C]//Proceedings of the 2023 AAAI Conference on Artificial Intelligence. Menlo Park: AAAI Press, 2023: 12 345-12 353.
[26]	CHEN Minze, TAO Zhenxiang, TANG Weitong, et al. Enhancing emergency decision-making with knowledge graphs and large language models[J]. International Journal of Disaster Risk Reduction, 2024, 113: 104 804.
[27]	Nadeem M, Zenkert J, Bender L, et al. KIRETT: knowledge-graph-based smart treatment assistant for intelligent rescue operations[EB/OL]. (2025-09-08). https://arxiv.org/abs/2508.07834.
[28]	MU Heng, WU Peng, SU Wenyi. Construction of knowledge graph for emergency resources[J]. International Journal of Intelligent Systems, 2024, 2024(1): 6668559.
[29]	CHEN Yiyue, ZHANG Laibin, HU Jinqiu, et al. An emergency task recommendation model of long-distance oil and gas pipeline based on knowledge graph convolution network[J]. Process Safety and Environmental Protection, 2022, 167: 651-661.
[30]	HU Ankang, LI Kaiwen, WU Zhen, et al. Intelligent assistant in radiation protection based on large language model with knowledge base[J]. Radiation and Environmental Biophysics, 2025, 64(3): 519-529.
[31]	徐浩. 辐射相关基因知识图谱的构建与应用[D]. 北京: 中国人民解放军军事科学院, 2020.
[32]	李智妍. 面向核污水排海事件的事件图谱构建研究[D]. 武汉: 中南财经政法大学, 2024.