{
  "abstract": "BACKGROUND: Many transfusion reactions are not reported to hemovigilance systems, and the features needed to recognize them are often documented only in free-text clinical notes. We sought to develop and evaluate an open-source, multi-agent artificial intelligence (AI) pipeline to extract transfusion reaction features defined by the National Healthcare Safety Network (NHSN) Hemovigilance Module from clinical notes. METHODS: We curated 181 features from the NHSN protocol and developed a multi-stage pipeline that extracted features, their timing, and clinical details from free-text notes. Three open-weight large language models (Qwen 3.6-27B, GPT-OSS-120B, and Kimi K3) were applied to 50 notes from expert-written scenarios, published case reports, and MIMIC-IV discharge summaries. Two physicians independently evaluated every model output in a blinded interface. The primary analysis was restricted to feature occurrences with concordant physician assessments. RESULTS: Physicians agreed on 1,201 of 1,343 co-rated feature occurrences (89.4%; Krippendorff's alpha 0.73). Precision was above 0.98 for all models. Sensitivity was 0.774 (95% CI 0.745-0.800) for Kimi K3, 0.636 (0.604-0.667) for GPT-OSS-120B, and 0.624 (0.593-0.655) for Qwen 3.6-27B, and specificity ranged from 0.952 to 0.991. Omissions accounted for more than 94% of false negatives. Timing and clinical details were judged supported or reasonable in more than 92% and 97% of assessments, respectively. Results were consistent across note sources and in analyses including discordant assessments. CONCLUSION: Multi-agent AI pipelines provide a feasible method for extracting transfusion reaction signals from clinical notes with high precision. Our work may have applications in active hemovigilance, reaction classification, and big data research.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 15,
  "authors": [
    {
      "name": "A. Kumari"
    },
    {
      "name": "J. W. Jacobs"
    },
    {
      "name": "L. Dilly"
    },
    {
      "name": "A. Anand"
    },
    {
      "name": "J. Woo"
    },
    {
      "name": "Y. Lin"
    },
    {
      "name": "A. W. Shih"
    },
    {
      "name": "G. S. Booth"
    },
    {
      "name": "I. Raslan"
    },
    {
      "name": "N. Relke"
    },
    {
      "name": "R. Dubey"
    },
    {
      "name": "S. Srivastava"
    },
    {
      "name": "W. Abdulwahab"
    },
    {
      "name": "R. Goel"
    },
    {
      "affs": [
        0
      ],
      "name": "S. Raza"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7py4y9kaz691k.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.06.26367389",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.11.06.26367389v1.full",
  "id": "7py4y9kaz691k",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.11.06.26367389v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.11.06.26367389v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7py4y9kaz691k.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T00:22:41Z",
      "native_id": "10.64898/2026.11.06.26367389",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.11.06.26367389v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Multi-Agent Generative Artificial Intelligence for Transfusion Reaction Feature Extraction",
  "title_text": "Multi-Agent Generative Artificial Intelligence for Transfusion Reaction Feature Extraction",
  "type": "preprint",
  "updated_at": "2026-11-10T00:22:41Z",
  "url": "https://research.draconitic.com/paper/7py4y9kaz691k",
  "venue": "medRxiv",
  "versions": []
}