NICHE Research Group, Faculty of Computer Science, Dalhousie University.
Dept. of Community Health and Epidemiology, Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada.
Stud Health Technol Inform. 2021 May 27;281:392-396. doi: 10.3233/SHTI210187.
This paper proposes an automated knowledge synthesis and discovery framework to analyze published literature to identify and represent underlying mechanistic associations that aggravate chronic conditions due to COVID-19. We present a literature-based discovery approach that integrates text mining, knowledge graphs and ontologies to discover semantic associations between COVID-19 and chronic disease concepts that were represented as a complex disease knowledge network that can be queried to extract plausible mechanisms by which COVID-19 may be exacerbated by underlying chronic conditions.
本文提出了一种自动化的知识综合和发现框架,以分析已发表的文献,识别和表示因 COVID-19 而加重的慢性疾病的潜在机制关联。我们提出了一种基于文献的发现方法,该方法集成了文本挖掘、知识图谱和本体论,以发现 COVID-19 和慢性疾病概念之间的语义关联,这些关联被表示为一个复杂的疾病知识网络,可以通过该网络查询提取 COVID-19 可能因潜在慢性疾病而加重的合理机制。