通过多粒度概念激活与分层知识图谱融合，提升罕见病诊断准确率。
A Multi-granularity Concept Sparse Activation and Hierarchical Knowledge Graph Fusion Framework for Rare Disease Diagnosis
- 设计多粒度稀疏激活机制，精准触发医学概念。
- 在BioASQ数据集上准确率达0.89，接近临床阈值0.90。
- 适合需要深度医学推理的罕见病诊断系统开发者。
尽管医疗大模型在医疗领域取得进展，罕见病诊断仍受限于知识表征深度不足、概念理解有限和临床推理能力弱。本文提出一种结合多粒度稀疏激活与分层知识图谱的框架。四种互补匹配算法、多样性控制及五级回退策略实现精确概念激活；三层知识图谱（分类体系、临床特征、实例）提供结构化且实时更新的上下文。在BioASQ罕见病问答数据集上的实验显示，BLEU提升0.09，ROUGE提升0.05，准确率提升0.12，峰值准确率达0.89，接近0.90的临床阈值。专家评估证实信息质量、推理能力和专业表达均有提升，表明该方法可缩短罕见病患者的‘诊断之旅’。
原文摘要 · Abstract (English)
Despite advances from medical large language models in healthcare, rare-disease diagnosis remains hampered by insufficient knowledge-representation depth, limited concept understanding, and constrained clinical reasoning. We propose a framework that couples multi-granularity sparse activation of medical concepts with a hierarchical knowledge graph. Four complementary matching algorithms, diversity control, and a five-level fallback strategy enable precise concept activation, while a three-layer knowledge graph (taxonomy, clinical features, instances) provides structured, up-to-date context. Experiments on the BioASQ rare-disease QA set show BLEU gains of 0.09, ROUGE gains of 0.05, and accuracy gains of 0.12, with peak accuracy of 0.89 approaching the 0.90 clinical threshold. Expert evaluation confirms improvements in information quality, reasoning, and professional expression, suggesting our approach shortens the "diagnostic odyssey" for rare-disease patients.
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