A Multi-granularity Concept Sparse Activation and Hierarchical Knowledge Graph Fusion Framework for Rare Disease Diagnosis
Journal:
arXiv
Published Date:
Jul 11, 2025
Abstract
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.
