CONORM: Context-Aware Entity Normalization for Adverse Drug Event Detection

Adverse drug events (ADEs) are a critical aspect of patient safety and pharmacovigilance, with significant implications for patient outcomes and public health monitoring. The increasing availability of electronic health records, social media, and patient forums provides valuable yet challenging unstructured data sources for ADE surveillance. To address these challenges, we introduce CONORM, a framework integrating named entity recognition (NER) and normalization (EN) for ADEs across diverse textual domains. CONORM comprises CONORM-NER and CONORM-EN, featuring a dual-encoder with dynamic context refining (DCR). DCR adaptively combines isolated entity embeddings with contextual representations. Our analyses demonstrate that CONORM adapts to text formality, enhances precision on out-of-distribution concepts, and reduces errors compared to context-agnostic baselines. CONORM was evaluated on tweets, forum posts, and structured product labels, covering 30,000 annotated ADE mentions across the SMM4H, CADEC, and TAC datasets. It achieved end-to-end F1-scores of 63.86%, 72.45%, and 84.99%, surpassing the strongest baseline by an average margin of 20%. These results highlight CONORM's robust adaptability across domains. CONORM offers a scalable solution for pharmacovigilance, with pre-computed target embeddings enhancing inference efficiency. Its generalization establishes it as a robust tool for ADE surveillance. The source code is available at https://github.com/ds4dh/CONORM .