{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [
    "National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China",
    "Institute of Artificial Intelligence, Xiamen University, Xiamen, China",
    "School of Medicine, Xiamen University, Xiamen, China",
    "School of Pharmaceutical Sciences, Xiamen University, Xiamen, China",
    "Kindstar Globalgene Technology, Inc., Wuhan, China",
    "Department of Paediatrics, Women and Children’s Hospital, School of Medicine, Xiamen University, Xiamen, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Guiling Guo"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Zhenyu Shao"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Heying Luo"
    },
    {
      "affs": [
        0,
        3
      ],
      "name": "Ziqing Fu"
    },
    {
      "affs": [
        4
      ],
      "name": "Hui Xiong"
    },
    {
      "affs": [
        0
      ],
      "name": "Jin Li"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Qingqing Qin"
    },
    {
      "affs": [
        5
      ],
      "name": "Xiaoqing Yang"
    },
    {
      "affs": [
        5
      ],
      "name": "Shuxiang Hu"
    },
    {
      "affs": [
        5
      ],
      "name": "Jinzhun Wu"
    },
    {
      "affs": [
        0,
        5
      ],
      "name": "Qiyuan Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7k3xysmnpsyph.bib",
  "cited_by": 0,
  "doi": "10.1038/s41746-026-02943-5",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13737159/",
  "id": "7k3xysmnpsyph",
  "issns": [
    "2398-6352"
  ],
  "landing_url": "https://doi.org/10.1038/s41746-026-02943-5",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
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  "openalex": "W7167084828",
  "pdf_url": "https://www.nature.com/articles/s41746-026-02943-5_reference.pdf",
  "pmcid": "PMC13737159",
  "pmid": "42393263",
  "published": "2026-07-02",
  "publisher": "Nature Publishing Group",
  "ris": "https://research.draconitic.com/paper/7k3xysmnpsyph.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:22:26Z",
      "native_id": "PMC13737159",
      "source": "pmc",
      "url": "https://doi.org/10.1038/s41746-026-02943-5"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "KGRD: a knowledge-graph-augmented automated reasoning framework for diagnosis and counselling of paediatric rare genetic disorders",
  "title_text": "KGRD: a knowledge-graph-augmented automated reasoning framework for diagnosis and counselling of paediatric rare genetic disorders",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:22:26Z",
  "url": "https://research.draconitic.com/paper/7k3xysmnpsyph",
  "venue": "NPJ Digital Medicine",
  "versions": []
}