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<p>Diagnosis and counselling for paediatric rare diseases remain constrained by the sparsity of structured patient-level data and fragmented genetic knowledge, which can induce ‘common-attention’ bias in conventional large language models (LLMs). Here, we describe KGRD, a knowledge-graph-augmented diagnostic support framework empowered by knowledge-driven and data-driven inference over patient-level genomic and phenotypic data. KGRD consists of three specialised inference agents for deductive reasoning regarding disease aetiology, as well as a collective decision-making module that integrates multidisciplinary deliberation with multi-source verification. In a validation benchmark of 420 rare disease cases, KGRD(DS) achieved the strongest overall performance, increased the mean Bond score of the top-ranked diagnosis from 3.27 to 3.85 and raised CIE from 73.6 to 81.9%, corresponding to 35 additional cases with candidate-diagnosis Bond score ≥4. Together, these results indicate that KGRD provides effective diagnostic support for paediatric rare diseases.</p>

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KGRD: a knowledge-graph-augmented automated reasoning framework for diagnosis and counselling of paediatric rare genetic disorders

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月2日

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  • Guiling Guo,
  • Zhenyu Shao,
  • Heying Luo,
  • Ziqing Fu,
  • Hui Xiong,
  • Jin Li,
  • Qingqing Qin,
  • Xiaoqing Yang,
  • Shuxiang Hu,
  • Jinzhun Wu,
  • Qiyuan Li

摘要

Diagnosis and counselling for paediatric rare diseases remain constrained by the sparsity of structured patient-level data and fragmented genetic knowledge, which can induce ‘common-attention’ bias in conventional large language models (LLMs). Here, we describe KGRD, a knowledge-graph-augmented diagnostic support framework empowered by knowledge-driven and data-driven inference over patient-level genomic and phenotypic data. KGRD consists of three specialised inference agents for deductive reasoning regarding disease aetiology, as well as a collective decision-making module that integrates multidisciplinary deliberation with multi-source verification. In a validation benchmark of 420 rare disease cases, KGRD(DS) achieved the strongest overall performance, increased the mean Bond score of the top-ranked diagnosis from 3.27 to 3.85 and raised CIE from 73.6 to 81.9%, corresponding to 35 additional cases with candidate-diagnosis Bond score ≥4. Together, these results indicate that KGRD provides effective diagnostic support for paediatric rare diseases.