GafRel: A Joint Entity and Relation Extraction Framework for Chinese Electronic Medical Records with Multidimensional Semantic Enhancement

Relation extraction from electronic medical records EMRs is essential for advancing biomedical information systems, but it remains challenging due to nested entity structures and limited contextual representations. To ad-dress these issues, we propose GafRel, a novel joint entity and relation ex-traction framework for Chinese EMRs. GafRel extends CasRel by integrat-ing a Global Pointer to better handle nested entity recognition. Additional-ly, we design a Multi-dimensional Feature Enhancement Layer MFEL , which enables multi-scale contextual modeling through semantic fusion of both local and global features. This architecture enhances the capacity of the model to capture local continuity and long-range dependencies. To address the lack of relation extraction datasets in Chinese EMRs, we construct Di-aRel, a new dataset derived from EMRs of 608 hospital patients. Experi-ments on CMeIE-v2, DiaKG, and DiaRel demonstrate the strong perfor-mance of our method, where GafRel outperforms existing baselines with F1 scores of 53.38 , 53.41 , and 83.98 on the three datasets, respectively. These results highlight the effectiveness of GafRel in extracting complex re-lations from EMRs and its potential for advancing biomedical information extraction.