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.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Manfred Hauben is a former employee of Pfizer Inc. and owns Pfizer stocks and stock options as well as stocks in other pharmaceutical companies that manufacture or market drugs mentioned in this article. Manfred Hauben has no other conflicts of interest that are relevant to the content of this manuscript. Mazin Rafi is a former Summer Associate of Pfizer Incorporate, and is currently pursuing an MSc in Data Science through Rutgers University. Mazin Rafi has no other conflicts of interest that are relevant to the content of this manuscript.
(Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)