川崎病知识图谱构建研究

Constructing Knowledge Graphs of Kawasaki Disease

川崎病是一种对儿童危害极大的血管炎综合征。川崎病可导致严重的心脏缺血症状或发展成缺血性心脏病,从而导致患儿死亡。尽管许多学者做了大量的研究,但病因始终未明,也没有发现一种特异性生物学标记物可以确诊。临床医生需要分析川崎病相关的各类复杂的知识与数据资源,以获得临床决策支持的信息,并做出有效的临床决策。知识图谱能够集成各类复杂的知识与数据资源,成为人工智能技术应用的一种重要方法。已经构建了川崎病的知识图谱,集成了与川崎病相关的多种知识资源,包括临床指南,临床实验的数据,药物知识库,医学文献,药物不良反应知识库,为川崎病的临床决策支持提供了基础的知识与数据资源整合基础。

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. Although many scholars have done a lot of research, but the etiology has not been identified and specific biological markers for confirmation of the diagnosis has not been found. Clinicians need to analyze Kawasaki＇s complex knowledge and data resources for clinical decision support and make effective clinical decisions. Knowledge Graphs have become an important AI approach to integrate various types of complex knowledge and data resources. We have constructed 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 decision support.