Natural Language Processing for Pharmacovigilance Signal Detection

Abstract

Pharmacovigilance increasingly relies on large volumes of unstructured clinical narratives, spontaneous safety reports, and digital health records, making conventional adverse drug event surveillance inefficient. This study proposes a novel Context-Aware Hierarchical Transformer with Signal Prioritization (CHT-SP) framework for early pharmacovigilance signal detection using natural language processing. Unlike existing approaches that mainly focus on adverse event extraction, the proposed framework jointly models contextual relationships among drugs, symptoms, temporal expressions, and clinical uncertainty to improve signal prioritization. A hybrid semantic attention mechanism is introduced to distinguish clinically meaningful safety signals from irrelevant textual noise. The proposed architecture is designed to support scalable surveillance across heterogeneous healthcare documents while maintaining interpretability. Recent advances in transformer-based language models, biomedical embeddings, and contextual learning motivate the proposed methodology. The study addresses an important research gap in integrating contextual semantic reasoning with pharmacovigilance signal ranking. The proposed framework aims to improve early detection accuracy, reduce false alerts, and strengthen post-marketing drug safety surveillance