EDT-MCFEF: a multi-channel feature fusion model for emergency department triage of medical texts

The objective of this model is to enhance the accuracy and efficiency of emergency department triage, thereby providing scientific and technological support to the emergency department. In this paper, two medical text datasets are employed for experimental validation: a self-built emergency department triage dataset and a medical literature abstract dataset. The emergency department triage dataset consists of 28,000 English-annotated samples from 11 clinical departments, while the medical literature abstract dataset is a publicly available dataset (https://huggingface.co/datasets/123rc/medical_text). The experimental findings demonstrate that the proposed model exhibits superior accuracy to seven benchmark models utilized in this study on both medical text datasets, indicating its efficacy in handling imbalanced datasets. This suggests enhanced generalization and robustness. In addition to its strong classification ability, the model exhibits favorable interpretability through its multi-channel design, and the hybrid masking strategy supports data minimization and privacy protection, aligning with ethical AI principles. This approach holds promise for integration into clinical decision support systems for improved triage accuracy. The models and the self-built dataset presented in this paper are available at https://github.com/Yiii-master/EDT-MCFEF.