{
  "abstract": "Oncology knowledge spans structured and unstructured datasets of drugs, regimens, conditions, and identifiers, as well as clinician-authored narratives describing treatment sequencing across disease settings. Answering clinical questions may require accessing both databases and free-text narratives, but existing intelligent retrieval systems favor either relational facts or narrative context. We developed HemOncAgent, an artificial intelligence agent that selects among retrieval tools for the HemOnc knowledge ecosystem: HemOncKB, a curated knowledge graph for pharmacologic relationships, and HemOnc.org, a narrative resource for care pathways. Across structured and narrative benchmarks, HemOncAgent maintained high-fidelity retrieval across question types for which single-source systems showed domain-specific limitations, supporting hybrid tool-based retrieval for more reliable oncology knowledge access.",
  "abstract_shown": true,
  "affiliations": [
    "Center for Clinical Cancer Informatics and Data Science, Legorreta Cancer Center, Brown University"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "name": "A. J. Yang"
    },
    {
      "name": "H. A. Zaki"
    },
    {
      "name": "A. Seto"
    },
    {
      "name": "T. Kim"
    },
    {
      "name": "I. B. Riaz"
    },
    {
      "name": "A. Srisuwananukorn"
    },
    {
      "name": "A. J. Cowan"
    },
    {
      "name": "P. C. Yang"
    },
    {
      "affs": [
        0
      ],
      "name": "J. L. Warner"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9qwnq4ywqjg8j.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.09.26362651",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.09.26362651v1.full",
  "id": "9qwnq4ywqjg8j",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.09.26362651v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7212361692",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.09.26362651v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9qwnq4ywqjg8j.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:21Z",
      "native_id": "10.64898/2026.09.09.26362651",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.09.26362651v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "HemOncAgent: an artificial intelligence system for retrieving structured and narrative oncology knowledge",
  "title_text": "HemOncAgent: an artificial intelligence system for retrieving structured and narrative oncology knowledge",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:21Z",
  "url": "https://research.draconitic.com/paper/9qwnq4ywqjg8j",
  "venue": "medRxiv",
  "versions": []
}