@inproceedings{machado-etal-2026-dataset,
    title = "A Dataset of {B}razilian {P}ortuguese Clinical Notes for Anaphylaxis Detection",
    author = "Machado, Matheus  and
      Vanzin, Vin{\'i}cius  and
      Moreira, Dilvan  and
      Ensina, Luis Felipe  and
      Lario, F{\'a}bio",
    editor = "Souza, Marlo  and
      de-Dios-Flores, Iria  and
      Santos, Diana  and
      Freitas, Larissa  and
      Souza, Jackson Wilke da Cruz  and
      Ribeiro, Eug{\'e}nio",
    booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 2",
    month = apr,
    year = "2026",
    address = "Salvador, Brazil",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-dnd/2026.propor-2.15/",
    pages = "78--87",
    ISBN = "979-8-89176-387-6",
    abstract = "Anaphylaxis is an acute, potentially life-threatening allergic reaction that requires rapid recognition in clinical settings. Natural language processing (NLP) approaches for automatic detection of anaphylaxis in clinical narratives can support large-scale analysis of health records and retrospective clinical research. However, such approaches depend on high-quality labeled corpora, and resources for Portuguese remain scarce. This paper introduces a corpus of Brazilian Portuguese clinical notes annotated by domain specialists for the presence or absence of anaphylaxis. The dataset comprises 969 clinical narratives drawn from three sources: clinician-authored synthetic clinical notes designed to represent realistic scenarios, case reports from the medical literature rewritten into note-like format by specialists, and a subset of de-identified notes from the publicly available SemClinBr corpus. All texts were reviewed and labeled by allergists using established clinical diagnostic criteria, and the corpus reflects realistic prevalence conditions, with approximately 5{\%} positive cases. We describe the corpus design, data sources, annotation methodology, and composition, discuss potential research applications, and address ethical considerations. The corpus is intended as a reusable resource for Portuguese clinical NLP, supporting future work on document classification, information extraction, and language modeling in the medical domain."
}