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Knowledge Graphs of Kawasaki Disease.
Huang, Zhisheng; Hu, Qing; Liao, Mingqun; Miao, Cong; Wang, Chengyi; Liu, Guanghua.
  • Huang Z; Knowledge Representation and Reasoning (KR&R) Group, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
  • Hu Q; School of Computer Science and Engineering, Wuhan University of Science and Technology, Wuhan, China.
  • Liao M; Ztone International BV, Purmerend, The Netherlands.
  • Miao C; Knowledge Representation and Reasoning (KR&R) Group, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
  • Wang C; School of Computer Science and Engineering, Wuhan University of Science and Technology, Wuhan, China.
  • Liu G; Ztone International BV, Purmerend, The Netherlands.
Health Inf Sci Syst ; 9(1): 11, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1375014
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.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Health Inf Sci Syst Year: 2021 Document Type: Article Affiliation country: S13755-020-00130-8

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Health Inf Sci Syst Year: 2021 Document Type: Article Affiliation country: S13755-020-00130-8