CARE: Contextual Residual and soft Encoding Relation Extraction for clinical medicine

Xin Guo, Kaifei Li, Qian Chen, Suge Wang, Jian Liao, Yang Gu

Abstract: Clinical event extraction involves extracting event attributes from clinical medical records. However, arguments involved in clinical events exhibit specificity, diversity and ambiguity, posing substantial challenges for existing models. The scarcity of Chinese clinical datasets further impedes research on clinical event extraction. Existing models commonly experience issues of contextual information decay during multi-task processes. Furthermore, unlike entities in general domains, medical entities are expressed in complex ways, resulting in low recall. To address these challenges, we propose a Clinical Event Extraction model based on Contextual ResiduAl and Soft Encoding Relation Extraction(CARE), which consists of an encoding module, a relation detection module, and an entity recognition module. The relation detection module identifies potential relations within a medical record. To prevent error propagation in relation extraction, a soft encoding strategy is proposed to discern target relations from candidate ones. The entity recognition module employs the contextual residual connection mechanism to concatenate the text with relation between semantic templates before feeding them into the entity recognition module. On CHIP-CDEE and CEMRs, CARE achieves F1 scores of 73.22% and 94.83%, respectively, which outperforms all baseline models, including LLMs, demonstrating its effectiveness for this task.

External IDs:dblp:conf/ijcnn/GuoLCWLG25