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… Click to show full abstract
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.
Journal Title: Studies in health technology and informatics
Year Published: 2022
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