RESUMEN

The advancement of medical informatization necessitates extracting entities and their relationships from electronic medical records. Presently, research on electronic medical records predominantly concentrates on single-entity relationship extraction. However, clinical electronic medical records frequently exhibit overlapping complex entity relationships, thereby heightening the challenge of information extraction. To rectify the absence of a clinical medical relationship extraction dataset, this study utilizes electronic medical records from 584 patients in a hospital to create a compact clinical medical relationship extraction dataset. To address the pipelined relationship extraction model's limitation in overlooking the one-to-many correlation problem between entities and relationships, this paper introduces a cascading relationship extraction model. This model integrates the MacBERT pre-training model, gated recurrent network, and multi-head self-attention mechanism to enhance the extraction of text features. Simultaneously, adversarial learning is incorporated to bolster the model's robustness. In scenarios involving one-to-many relationships between entities, a two-phase task is employed. Initially, the main entity is predicted, followed by predicting the associated object and their correspondences. Employing this cascade-structured approach enables the model to flexibly manage intricate entity relationships, thereby enhancing extraction accuracy. Experimental results demonstrate the model's efficiency, yielding F1-scores of 82.8%, 76.8%, and 88.2% for fulfilling relational extraction requirements and tasks on DuIE, CHIP-CDEE, and private datasets, respectively. These scores represent improvements over the benchmark model. The findings indicate the model's applicability in practical domains, particularly in tasks such as biomedical information extraction.