NYMC Faculty Publications

Knowledge Graphs in Pharmacovigilance: A Scoping Review

Author Type(s)

Faculty

DOI

10.1016/j.clinthera.2024.06.003

Journal Title

Clinical Therapeutics

First Page

544

Last Page

554

Document Type

Article

Publication Date

7-1-2024

Department

Family and Community Medicine

Keywords

Adverse drug reactions, Drug safety, Graph machine learning, Knowledge graphs, Pharmacovigilance, Scoping review

Disciplines

Medicine and Health Sciences

Abstract

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.

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