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

arXiv 2507.08529 ↗First posted 11 Jul 2025cs.AI cs.CLcited by 0

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Abstract (arXiv)

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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References · 11

  1. 2022Large Language Models Encode Clinical Knowledgecited by 1,830
  2. 2019Publicly Available Clinical BERT Embeddingscited by 1,002
  3. 2018Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentationcited by 146
  4. 2024A Framework for Human Evaluation of Large Language Models in Healthcare Derived from Literature Reviewcited by 66
  5. 2024Large Language Models for Disease Diagnosis: A Scoping Reviewcited by 51
  6. 2024RareBench: Can LLMs Serve as Rare Diseases Specialists?cited by 45
  7. 2023Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Studycited by 30
  8. 2021Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from textscited by 7
  9. 2024A Hybrid Framework with Large Language Models for Rare Disease Phenotypingcited by 7
  10. 2024Zebra-Llama: A Context-Aware Large Language Model for Democratizing Rare Disease Knowledgecited by 3
  11. 2025Rare Disease Differential Diagnosis with Large Language Models at Scale: From Abdominal Actinomycosis to Wilson's Diseasecited by 2

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