A Multi-granularity Concept Sparse Activation and Hierarchical Knowledge Graph Fusion Framework for Rare Disease Diagnosis

Rare disease diagnosis remains challenging for medical large language models due to insufficient knowledge representation, limited concept understanding, and constrained clinical reasoning. We propose a framework combining multi-granularity sparse activation with hierarchical knowledge graphs. Our approach employs four complementary matching algorithms with diversity control and a five-level fallback strategy for precise concept activation. A three-layer knowledge graph (taxonomy, clinical features, instances) provides structured, up-to-date context. Experiments on the BioASQ rare disease dataset demonstrate significant improvements: BLEU scores increased by up to 0.13, ROUGE by up to 0.10, and diagnostic accuracy by up to 0.25, with the best model achieving 0.92 accuracy--surpassing the 0.90 clinical threshold. Expert evaluation confirms enhancements in information quality, reasoning, and professional expression. Our framework shows promise in reducing the diagnostic odyssey for rare disease patients.