用外部知识图谱增强BERT，提升疾病诊断准确率与可解释性
A Knowledge-Enhanced Disease Diagnosis Method Based on Prompt Learning and BERT Integration
- 从知识图谱提取结构化临床知识，注入提示模板增强模型理解
- 在三个数据集上F1分别提升2.4%、3.1%和4.2%
- 适合需要可解释性医疗AI的临床辅助诊断场景
本文提出一种基于提示学习与BERT融合的知识增强疾病诊断方法。该方法从与临床病例相关的外部知识图谱中检索结构化知识，进行编码后注入提示模板，以增强语言模型对任务的理解与推理能力。我们在CHIP-CTC、IMCS-V2-NER和KUAKE-QTR三个公开数据集上进行了实验，结果表明，所提方法在多个评估指标上显著优于现有模型：在CHIP-CTC数据集上F1提升2.4%，在IMCS-V2-NER数据集上提升3.1%，在KUAKE-QTR数据集上提升4.2%。消融实验进一步证实知识注入模块的关键作用，移除该模块导致F1显著下降。实验结果表明，该方法不仅有效提升了疾病诊断的准确性，还增强了预测的可解释性，为临床诊断提供了更可靠的支持与证据。
原文摘要 · Abstract (English)
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.
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