Knowledge Graphs in Pharmacovigilance: A Step-By-Step Guide.

PURPOSE: This work aims to demystify Knowledge Graphs (KGs) in pharmacovigilance (PV). It complements the scoping review within this issue. By bridging knowledge gaps and stimulating interest, further engagement with this topic by pharmacovigilance professionals will be facilitated. METHODS: We elucidate fundamental KGs concepts and terminology, followed by delineating a sequence of implementation steps: use case definition, data type selection, data sourcing, KG construction, KG embedding, and deriving actionable insights. Information technology options and limitations are also explored. FINDINGS: KGs in pharmacovigilance is a multi-disciplinary field involving information technology, machine learning, biology, and PV. We were able to synthesize the relevant core concepts to create an intuitive exposition of KGs in PV. IMPLICATIONS: This work demystifies KGs with a pharmacovigilance focus, preparing readers for the accompanying in-depth scoping review. that follows. It lays the groundwork for advancing PV research and practice by emphasizing the importance of engaging with vigilance experts. This approach enhances knowledge sharing and collaboration, contributing to more effective and informed pharmacovigilance efforts and optimal assessment and deployment of KGs in PV.

Knowledge Graphs in Pharmacovigilance: A Scoping Review.

PURPOSE: To critically assess the role and added value of knowledge graphs in pharmacovigilance, focusing on their ability to predict adverse drug reactions. METHODS: A systematic scoping review was conducted in which detailed information, including objectives, technology, data sources, methodology, and performance metrics, were extracted from a set of peer-reviewed publications reporting the use of knowledge graphs to support pharmacovigilance signal detection. FINDINGS: The review, which included 47 peer-reviewed articles, found knowledge graphs were utilized for detecting/predicting single-drug adverse reactions and drug-drug interactions, with variable reported performance and sparse comparisons to legacy methods. IMPLICATIONS: Research to date suggests that knowledge graphs have the potential to augment predictive signal detection in pharmacovigilance, but further research using more reliable reference sets of adverse drug reactions and comparison with legacy pharmacovigilance methods are needed to more clearly define best practices and to establish their place in holistic pharmacovigilance systems.