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 d