Focal-Loss Transformer with Feedback-Validation Learning for Imbalanced Drug Interaction Extraction

Abstract

Drug-drug interactions (DDIs) are a leading cause of preventable adverse drug events, and the standard benchmark corpus for extracting them from biomedical text is severely im-balanced towards non-interacting pairs, making rare interaction types hard to learn. We asked whether combining focal loss, entity-aware transformer encoding, and feedback-guided feature fusion in a single architecture could improve minority-class DDI extraction under this imbalance while requiring less training data than prior methods. We propose FoLT-DMCNN-FBVL, which integrates a BiomedBERT backbone with entity marker injection, a multi-branch neural classifier, and feedback-based validation learning, trained on a small balanced subset of the SemEval- 2013 DDIExtraction corpus and evaluated on the full blind test set. FoLT-DMCNN-FBVL matched the most recent state-of-the-art result (statistically indistinguishable; nominal absolute+0.33%) and significantly outperformed the widely cited CNN-DDI baseline (absolute +4.32%), while using less than one-fifth of the available training data. These findings show that a doubly imbalance-aware transformer architecture can match or exceed current state-of-the-art DDI extraction performance with substantially greater data efficiency. Such architectures could support more scalable and cost-effective pharmacovigilance and clinical NLP pipelines in settings where annotated data for rare interaction types is limited.