NICHE Research Group, Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada.
Department of Community Health and Epidemiology, Faculty of Medicine, Halifax, Nova Scotia, Canada.
Stud Health Technol Inform. 2022 Jun 6;290:304-308. doi: 10.3233/SHTI220084.
We present 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. Our literature-based discovery approach integrates text mining, knowledge graphs and medical ontologies to discover hidden and previously unknown pathophysiologic relations, dispersed across multiple public literature databases, between COVID-19 and chronic disease mechanisms. We applied our approach to discover mechanistic associations between COVID-19 and chronic conditions-i.e. diabetes mellitus and chronic kidney disease-to understand the long-term impact of COVID-19 on patients with chronic diseases. We found several gene-disease associations that could help identify mechanisms driving poor outcomes for COVID-19 patients with underlying conditions.
我们提出了一个自动化的知识综合和发现框架，用于分析已发表的文献，以识别和表示因 COVID-19 而加重慢性疾病的潜在机制关联。我们基于文献的发现方法集成了文本挖掘、知识图谱和医学本体，以发现隐藏的和以前未知的病理生理关系，这些关系分布在多个公共文献数据库中，涉及 COVID-19 和慢性疾病机制之间。我们应用我们的方法来发现 COVID-19 与慢性疾病（即糖尿病和慢性肾脏病）之间的机制关联，以了解 COVID-19 对患有慢性疾病的患者的长期影响。我们发现了一些基因-疾病关联，这些关联可能有助于识别导致患有基础疾病的 COVID-19 患者预后不良的机制。
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