Deep-Learning Approach for Safety Signal Detection in Pharmacovigilance

Music City Center 

Safety signal detection in pharmacovigilance often relies on traditional methods with limited capabilities in identifying complex dependencies and patterns in adverse event (AE) data. We propose a deep-learning algorithm using the DeepVARHierarchical model [1] for hierarchical multivariate time series learning and prediction, adapted to detect safety signals. This adapted model captures dependencies within and across hierarchical levels of MedDRA (SOC, HLGT, HLT, and PT) by learning intra-series and inter-series relationships. Empirical results demonstrate that our algorithm enhances the accuracy and sensitivity of signal detection while identifying safety signals earlier than traditional methods. This approach improves the efficiency and reliability of pharmacovigilance practices, enabling proactive risk management and improving patient safety by identifying complex AE patterns as they evolve over time.

Safety signal detection

Deep learning

DeepVARHierarchical

MedDRA

Artificial Intelligence 

Section on Statistical Learning and Data Science