A Knowledge-Enhanced Disease Diagnosis Method Based on Prompt Learning and BERT Integration
This work addresses disease diagnosis accuracy and interpretability for clinical applications, but it is incremental as it builds on existing prompt learning and BERT methods with knowledge injection.
This paper tackled the problem of disease diagnosis by proposing a knowledge-enhanced method using prompt learning and BERT integration, which improved F1 scores by 2.4% to 4.2% on three public datasets and enhanced interpretability for clinical support.
This paper proposes a knowledge-enhanced disease diagnosis method based on a prompt learning framework. The method retrieves structured knowledge from external knowledge graphs related to clinical cases, encodes it, and injects it into the prompt templates to enhance the language model's understanding and reasoning capabilities for the task.We conducted experiments on three public datasets: CHIP-CTC, IMCS-V2-NER, and KUAKE-QTR. The results show that the proposed method significantly outperforms existing models across multiple evaluation metrics, with an F1 score improvement of 2.4% on the CHIP-CTC dataset, 3.1% on the IMCS-V2-NER dataset,and 4.2% on the KUAKE-QTR dataset. Additionally,ablation studies confirmed the critical role of the knowledge injection module,as the removal of this module resulted in a significant drop in F1 score. The experimental results demonstrate that the proposed method not only effectively improves the accuracy of disease diagnosis but also enhances the interpretability of the predictions, providing more reliable support and evidence for clinical diagnosis.