$\mathrm{D}^{2}$KGMed: Dynamic Diagnostic Knowledge Graphs for Medical Diagnosis Prediction

Jie Zhang, Gaoyang Zheng, Hang Lv, Linhao Luo, Guofang Ma, zhigang lin, Xiping Chen, Yanchao Tan

Abstract: Accurate diagnosis prediction using Electronic Health Records (EHRs) is essential for personalized healthcare. Clinical knowledge graphs (KGs) can enrich EHRs by structuring medical knowledge, and recent work integrates large language models (LLMs) with KGs to enhance reasoning. However, these approaches often depends on static, expensive global graph construction and one-time retrieval, yielding noisy or irrelevant subgraphs that hinder effective diagnosis prediction in real-world clinical scenarios. To this end, we propose $\mathrm{D}^{2}$KGMed, a diagnosis prediction framework that constructs a patient-specific Dynamic Diagnostic Knowledge Graph guided by LLMs. It consists of two stages: constructing an initial graph from diagnostic entities and multi-source medical knowledge; refining its construction via supervised fine-tuning to better align with the ideal graph for conciseness and relevance, and subsequently leveraging it for interpretable predictions. This design reduces graph construction costs and retrieval noise common in KG+LLM methods, enabling more accurate diagnosis prediction. Extensive experiments on two real-world EHR datasets demonstrate that D2KGMed outperforms state-of-the-art baselines, especially in few-shot learning scenarios, showcasing its practical utility in real-world clinical settings.