CECRel: A joint entity and relation extraction model for Chinese electronic medical records of coronary angiography via contrastive learning.

Abstract

Entity and relation extraction from Chinese electronic medical records (EMRs) is a crucial foundation for constructing medical knowledge graphs and supporting downstream tasks. Chinese EMRs face challenges in accurately extracting medical entity relations due to limited data and the complexity of overlapping medical relationships. We propose CECRel, a joint extraction model for Chinese EMR entity relations based on contrastive learning and feature enhancement to address this issue. CECRel employs data augmentation strategies to generate positive and negative samples for contrastive loss computation and utilizes a feature enhancement module to enrich textual structural features, enabling the accurate extraction of complex relational triples. Experiments conducted on our constructed dataset, CACMeD, demonstrated that the model achieves an accuracy of 80.56%, a recall of 74.69%, and an F1 score of 77.51%. Furthermore, in the Baidu DuIE dataset, the model achieved an accuracy of 79.71%, a recall of 74.14%, and an F1 score of 76.82%, demonstrating that the proposed model is competitive among existing models.