| 引用本文: | 王楚童,李明达,孙孟轩,王静,杨雪冰,牛景昊,贺志阳,张文生.融合大规模医学事实的跨语言双层知识图谱.软件学报,2025,36(3):1240-1253 |
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| 融合大规模医学事实的跨语言双层知识图谱 |
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王楚童1,2, 李明达1, 孙孟轩1,2, 王静3, 杨雪冰1, 牛景昊1, 贺志阳3, 张文生1
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1.多模态人工智能系统全国重点实验室(中国科学院 自动化研究所), 北京100190;2.中国科学院大学 人工智能学院, 北京 100049;3.讯飞医疗科技股份有限公司, 安徽 合肥 230088
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| 摘要: |
| 得益于信息化技术的快速发展和医疗信息系统的普及, 医学数据库中积淀了海量的医学事实, 如患者临床诊疗事件以及医学专家共识等. 如何从医学事实中提炼出知识, 进而对其管理和合理利用, 是推进诊疗自动化和智能化的关键. 知识图谱作为一种新型的知识表示工具, 能够有效地挖掘和组织大规模医学事实中的信息, 受到医疗领域从业人员的广泛关注. 然而, 现有医疗知识图谱普遍存在规模小、限制多、可扩展性差等问题, 面向医学事实的知识表达能力有限. 为此, 提出一种双层医疗知识图谱架构, 通过对英文患者诊疗事件和中文专家共识进行信息抽取, 构建得到一个跨语言、多模态、动态更新、可拓展性强的10亿级医疗知识图谱, 可提供更加精准的智能医疗服务. |
| 关键词: 医学事实 医疗知识图谱 双层知识表示 信息抽取 知识融合 |
| DOI：10.13328/j.cnki.jos.007173 |
| 分类号:TP181 |
| 基金项目:科技创新2030—“新一代人工智能”重大项目(2018AAA0102100); 国家自然科学基金(62203437) |
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| Cross-language Bilayer Knowledge Graph with Large-scale Medical Facts |
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WANG Chu-Tong1,2, LI Ming-Da1, SUN Meng-Xuan1,2, WANG Jing3, YANG Xue-Bing1, NIU Jing-Hao1, HE Zhi-Yang3, ZHANG Wen-Sheng1
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1.State Key Laboratory of Multimodal Artificial Intelligence Systems (Institute of Automation, Chinese Academy of Sciences), Beijing 100190, China;2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China;3.Xunfei Healthcare Technology Co. Ltd., Hefei 230088, China
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| Abstract: |
| Benefiting from the rapid development of information technology and the widespread adoption of medical information systems, a vast amount of medical knowledge has been accumulated in medical databases, including patient clinical treatment events and medical expert consensus. It is crucial to extract knowledge from these medical facts and effectively manage and utilize them, which can advance the automation and intelligence of diagnosis and treatment. Knowledge graphs, as a novel knowledge representation tool, can effectively mine and organize information from abundant medical facts and have received extensive attention in the medical field. However, existing medical knowledge graphs often suffer from limitations such as small scale, numerous restrictions, poor scalability, and so on, leading to a limited ability to express knowledge from medical facts. To address these issues, this proposes a bilayer medical knowledge graph architecture and employs information extraction techniques on both English patient clinical treatment events and Chinese medical expert consensus to construct a billion-scale medical knowledge graph that is cross-lingual, multimodal, dynamically updated, and highly scalable, aiming to provide more accurate, intelligent medical services. |
| Key words: medical fact medical knowledge graph bilayer knowledge representation information extraction knowledge fusion |
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