RSTGP: A Relation-Aware and Span-Graph-Enhanced Method for Joint Entity and Relation Extraction from Medical Texts

Electronic health records (EHRs) contain abundant medical entities and relations, and their accurate extraction is essential for medical knowledge graph construction and clinical decision-support. However, medical texts are typically characterized by dense terminology, syntactically complex expressions, and frequent overlapping triplets, which significantly increase the difficulty of joint extraction. Although table-filling methods such as GPLinker have achieved strong performance, they remain limited in modeling cross-relation interactions, capturing latent structural dependencies among candidate spans, and reducing noise from redundant entity-pair combinations. To address these limitations, we propose RSTGP, a unified joint extraction framework built upon the GPLinker paradigm. The proposed framework integrates three key mechanisms: (1) a relation-aware scoring strategy that enhances cross-relation interaction and calibrates relation scores; (2) a span-level graph enhancement mechanism that captures latent structural dependencies among candidate spans; and (3) a type-constrained calibration mechanism that suppresses implausible subject–object combinations. Extensive experiments on the CMeIE and DuIE2.0 datasets demonstrate that RSTGP consistently outperforms seven representative baseline models and achieves F1 scores of 62.75% and 76.79%, respectively. In particular, the proposed model shows clear advantages in handling overlapping and multiple triplets, yielding a more robust performance in complex extraction scenarios.