CBRRel: A Chinese Medical Entity Relationship Extraction Model Combining Location Aware Attention and Feature Fusion

Entity-relation extraction from Chinese electronic medical records EMRs is essential for constructing medical knowledge graphs and enabling intelligent diagnosis and clinical decision-making. However, the presence of complex sentence structures, overlapping entities, and sparse annotations poses signif-icant challenges. To address these issues, we propose CBRRel, a joint extrac-tion model optimized for Chinese EMRs. The model integrates a UNet-based semantic fusion module to enhance multi-scale representation learning and improve boundary detection for complex entities. To further strengthen structural understanding, we introduce a relative position attention mecha-nism that effectively captures positional dependencies between entity pairs. In addition, we apply the Fast Gradient Method FGM adversarial training to improve robustness against input perturbations. Experimental results on the CACMeD dataset show that CBRRel achieves 80.67 precision, 74.13 re-call, and a 77.26 F1 score. On the DuIE public dataset, it achieves an F1 score of 76.74 , demonstrating strong capability in handling overlapping and complex relation scenarios. These results highlight the effectiveness of CBRRel and its potential for practical medical information extraction.