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川崎病的知识图谱。

Knowledge Graphs of Kawasaki Disease.

作者信息

Huang Zhisheng, Hu Qing, Liao Mingqun, Miao Cong, Wang Chengyi, Liu Guanghua

机构信息

Knowledge Representation and Reasoning (KR&R) Group, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.

School of Computer Science and Engineering, Wuhan University of Science and Technology, Wuhan, China.

出版信息

Health Inf Sci Syst. 2021 Feb 27;9(1):11. doi: 10.1007/s13755-020-00130-8. eCollection 2021 Dec.

Abstract

Kawasaki Disease is a vasculitis syndrome that is extremely harmful to children. Kawasaki Disease can cause severe symptoms of ischemic heart disease or develop into ischemic heart disease, leading to death in children. Researchers and clinicians need to analyze various knowledge and data resources to explore aspects of Kawasaki Disease. Knowledge Graphs have become an important AI approach to integrating various types of complex knowledge and data resources. In this paper, we present an approach for the construction of Knowledge Graphs of Kawasaki Disease. It integrates a wide range of knowledge resources related to Kawasaki Disease, including clinical guidelines, clinical trials, drug knowledge bases, medical literature, and others. It provides a basic integration foundation of knowledge and data concerning Kawasaki Disease for clinical study. In this paper, we will show that this disease-specific Knowledge Graphs are useful for exploring various aspects of Kawasaki Disease.

摘要

川崎病是一种对儿童危害极大的血管炎综合征。川崎病可引发严重的缺血性心脏病症状或发展为缺血性心脏病,导致儿童死亡。研究人员和临床医生需要分析各种知识和数据资源,以探索川崎病的各个方面。知识图谱已成为整合各类复杂知识和数据资源的重要人工智能方法。在本文中,我们提出了一种构建川崎病知识图谱的方法。它整合了与川崎病相关的广泛知识资源,包括临床指南、临床试验、药物知识库、医学文献等。它为临床研究提供了关于川崎病的知识和数据的基本整合基础。在本文中,我们将表明这种针对特定疾病的知识图谱对于探索川崎病的各个方面是有用的。

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