Introduction: Causality assessment of individual case safety reports (ICSRs) is an important step in pharmacovigilance case-level review and aims to establish a position on whether a patient's exposure to a drug is causally related to the patient experiencing an untoward adverse event. There are many different approaches for case causality adjudication, including the use of expert opinions and algorithmic frameworks; however, a great deal of variability exists between assessment methods, products, therapeutic classes, individual physicians, change of process and conventions over time, and other factors.
Objective: The objective of this study was to develop a machine learning-based model that can predict the likelihood of a causal association of an observed drug-reaction combination in an ICSR.