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

TL;DR

This paper introduces a hierarchical knowledge graph and sparse concept activation framework to improve rare disease diagnosis, enhancing medical reasoning and information accuracy in large language models.

Contribution

It presents a novel multi-granularity sparse activation method combined with a hierarchical knowledge graph for better rare disease diagnosis.

Findings

BLEU score increased by 0.09

ROUGE score increased by 0.05

accuracy improved by 0.12, reaching 0.89

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.