A Knowledge Graph of Mechanistic Associations Between COVID-19, Diabetes Mellitus, and Chronic Kidney Disease.

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.

Building a Knowledge Graph Representing Causal Associations Between Risk Factors and Incidence of Breast Cancer.

This paper explores the use of semantic- and evidence-based biomedical knowledge to build the RiskExplorer knowledge graph that outlines causal associations between risk factors and chronic disease or cancers. The intent of this work is to offer an interactive knowledge synthesis platform to empower health-information-seeking individuals to learn about and mitigate modifiable risk factors. Our approach analyzes biomedical text (from PubMed abstracts), Semantic Medline database, evidence-based semantic associations, literature-based discovery, and graph database to discover associations between risk factors and breast cancer. Our methodological framework involves (a) identifying relevant literature on specified chronic diseases or cancers, (b) extracting semantic associations via knowledge mining tool, (c) building rich semantic graph by transforming semantic associations to nodes and edges, (d) applying frequency-based methods and using semantic edge properties to traverse the graph and identify meaningful multi-node NCD risk paths. Generated multi-node risk paths consist of a source node (representing the source risk factor), one or more intermediate nodes (representing biomedical phenotypes), a target node (representing a chronic disease or cancer), and edges between nodes representing meaningful semantic associations. The results demonstrate that our methodology is capable of generating biomedically valid knowledge related to causal risk and protective factors related to breast cancer.

A Knowledge Graph of Mechanistic Associations Between COVID-19, Diabetes Mellitus and Kidney Diseases.

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.

Towards Personalized Lifetime Health: A Platform for Early Multimorbid Chronic Disease Risk Assessment and Mitigation.

Chronic diseases are the leading cause of morbidity, disability and mortality worldwide. It is well established that the majority of chronic diseases can be prevented by targeting modifiable lifestyle-related risk factors. Thus, early risk assessment and mitigation at the individual level can significantly reduce the health and economic burden of chronic diseases. Lifetime health has emerged as a potential paradigm to assist individuals to avoid harmful lifestyle-related habits to reduce the risk of chronic morbidity. In this paper, we leverage eHealth and Quantified Self technologies, novel health data visualizations, and artificial intelligence methods to develop a digital-based lifetime health platform (PRISM) to empower individuals to self-assess, self-monitor, and self-manage their risks for multiple chronic diseases and associated morbidities.

A Personalized Risk Stratification Platform for Population Lifetime Healthcare.

Chronic diseases are the leading cause of death worldwide. It is well understood that if modifiable risk factors are targeted, most chronic diseases can be prevented. Lifetime health is an emerging health paradigm that aims to assist individuals to achieve desired health targets, and avoid harmful lifecycle choices to mitigate the risk of chronic diseases. Early risk identification is central to lifetime health. In this paper, we present a digital health-based platform (PRISM) that leverages artificial intelligence, data visualization and mobile health technologies to empower citizens to self-assess, self-monitor and self-manage their overall risk of major chronic diseases and pursue personalized chronic disease prevention programs. PRISM offers risk assessment tools for 5 chronic conditions, 2 psychiatric disorders and 8 different cancers.