CKD-EHR:Clinical Knowledge Distillation for Electronic Health Records

• URL: http://arxiv.org/abs/2506.15118v1
• Date: Wed, 18 Jun 2025 03:35:24 GMT
• Title: CKD-EHR:Clinical Knowledge Distillation for Electronic Health Records
• Authors: Junke Wang, Hongshun Ling, Li Zhang, Longqian Zhang, Fang Wang, Yuan Gao, Zhi Li,
• Abstract summary: Existing large language models face two major challenges: insufficient representation of medical knowledge and low efficiency in clinical deployment.<n>This study proposes the CKD-EHR framework, which achieves efficient and accurate disease risk prediction through knowledge distillation techniques.
• Score: 16.68137505931177
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electronic Health Records (EHR)-based disease prediction models have demonstrated significant clinical value in promoting precision medicine and enabling early intervention. However, existing large language models face two major challenges: insufficient representation of medical knowledge and low efficiency in clinical deployment. To address these challenges, this study proposes the CKD-EHR (Clinical Knowledge Distillation for EHR) framework, which achieves efficient and accurate disease risk prediction through knowledge distillation techniques. Specifically, the large language model Qwen2.5-7B is first fine-tuned on medical knowledge-enhanced data to serve as the teacher model.It then generates interpretable soft labels through a multi-granularity attention distillation mechanism. Finally, the distilled knowledge is transferred to a lightweight BERT student model. Experimental results show that on the MIMIC-III dataset, CKD-EHR significantly outperforms the baseline model:diagnostic accuracy is increased by 9%, F1-score is improved by 27%, and a 22.2 times inference speedup is achieved. This innovative solution not only greatly improves resource utilization efficiency but also significantly enhances the accuracy and timeliness of diagnosis, providing a practical technical approach for resource optimization in clinical settings. The code and data for this research are available athttps://github.com/209506702/CKD_EHR.

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