Enhanced Adverse-Event Detection and Drug-Event Relation Extraction from Clinical Notes.

Adverse drug events (ADEs) are a significant source of preventable patient harm, yet many ADE signals remain buried in free-text clinical notes. Clinical notes often describe adverse events (AEs) in relation to drugs in two ways: whether a drug causes the AE (the AE is an ADE) or a drug is given to treat an AE (it is considered the Reason for drug treatment). In the N2C2 2018 benchmark, ADEs and Reasons are annotated as separate entity types, despite often being similar in both wording and clinical meaning. This shared similarity makes them difficult to distinguish during entity extraction, leading to errors in relation classification. Therefore, we propose a two-stage framework that first detects AEs as a unified event category and then classifies drug-event pairs into Drug-ADE, Drug-Reason, or No-Relation. In the end-to-end evaluation on the N2C2 2018 benchmark, our system achieves F1 scores of 0.93 for Drug-ADE and 0.94 for Drug-Reason, improving over previously reported end-to-end benchmarks of 0.48 for Drug-ADE and 0.59 for Drug-Reason. Overall, these results support a more precise task formulation in which AEs are detected broadly first, and the ADE vs Reason distinction is resolved at the relation layer. Furthermore, they motivate the development of AE-focused datasets annotated independently of drug linkage to enable more reliable end-to-end pharmacovigilance systems.