--- title: "A Dataset of Brazilian Portuguese Clinical Notes for Anaphylaxis" description: "The authors build and document a public Brazilian Portuguese resource of 969 clinical narratives. 1 reference." authors: ["Matheus Machado", "Vinícius Vanzin", "Dilvan Moreira", "Luis Felipe Ensina", "Fábio Lario"] year: 2026 venue: "Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 2" source: "https://aclanthology.org/2026.propor-2.15/" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/machado-2026-dataset-brazilian-portuguese-clinical-notes/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/machado-2026-dataset-brazilian-portuguese-clinical-notes/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/20/) / A Dataset of Brazilian Portuguese Clinical Notes for Anaphylaxis Detection # A Dataset of Brazilian Portuguese Clinical Notes for Anaphylaxis Detection **Authors:** Matheus Machado, Vinícius Vanzin, Dilvan Moreira, Luis Felipe Ensina and Fábio Lario · **Year:** 2026 · **Venue:** [Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 2](https://md.research.elamur.ai/free/en/venues/propor/) ([International Conference on Computational Processing of Portuguese 2026](https://md.research.elamur.ai/free/en/venues/propor/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.propor-2.15/) · [PDF](https://aclanthology.org/2026.propor-2.15.pdf) ## Summary Automatically spotting anaphylaxis in free-text medical records could support large-scale retrospective research. Supervised methods, however, need labeled data, and Portuguese clinical corpora with document-level condition labels are very rare. SemClinBr, for instance, is annotated only for entities and relations. The authors build and document a public Brazilian Portuguese resource of 969 clinical narratives. Allergists tagged each one as anaphylaxis present or absent, applying the NIAID/FAAN and WAO 2020 diagnostic criteria. The texts come from three origins: - fictional notes written by hand by specialists, with no language models involved; - published case reports recast in a clinical-note style; - de-identified notes drawn from SemClinBr. Positive cases make up roughly 5% of the data. This mirrors real-world prevalence and turns the task into a difficult, imbalanced rare-event problem. The paper is deliberately dataset-centric and reports no new classifiers. Instead, it details provenance, annotation procedure, composition, limitations and ethics. The result is a reusable benchmark for document classification, information extraction and LLM evaluation in Portuguese clinical NLP, a field that otherwise depends heavily on private or restricted data. ## Key points - **Size and labels:** 969 Brazilian Portuguese clinical narratives with a binary label only. Severity, triggers, time course and outcomes are not annotated. - **Class balance:** about 5% positive cases, consistent with prevalence estimates cited from Cardona et al. (2020). - **Sources:** clinician-written synthetic notes (manual, not LLM-generated), literature case reports adapted into note form, and a de-identified subset of SemClinBr (a corpus of 1,000 notes with 65,117 entities). - **Annotation and privacy:** allergists of the University of São Paulo / Hospital Sírio-Libanês team labeled the texts using NIAID/FAAN and WAO criteria. The SemClinBr portion complies with Brazil's General Data Protection Law. - **Availability:** released on Hugging Face as `matos1012/brazilian-portuguese-anaphylaxis`. The authors flag the modest size, possible stylistic bias in synthetic notes, and the dataset's research-only scope. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* 📚 Shelves (2): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works ## Cite Cite as: Matheus Machado, Vinícius Vanzin, Dilvan Moreira, Luis Felipe Ensina and Fábio Lario. 2026. A Dataset of Brazilian Portuguese Clinical Notes for Anaphylaxis Detection. Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 2. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{machado2026dataset, title = {{A Dataset of Brazilian Portuguese Clinical Notes for Anaphylaxis Detection}}, author = {Matheus Machado, Vinícius Vanzin, Dilvan Moreira, Luis Felipe Ensina and Fábio Lario}, booktitle = {Proceedings of the 17th International Conference on Computational Processing of Portuguese (PROPOR 2026) - Vol. 2}, year = {2026}, url = {https://aclanthology.org/2026.propor-2.15/} } ``` - [← previous A Dataset for Oral Reading in Young English Readers](https://md.research.elamur.ai/free/en/papers/rose-2026-dataset-oral-reading-young-english/) - [next → A Dataset of Psychiatric Hospital Notes with Temporal Information Annotations](https://md.research.elamur.ai/free/en/papers/miller-2026-dataset-psychiatric-hospital-notes-temporal/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/20/) ## References 1 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Edmon Begoli, Kris Brown, Sudarshan Srinivas, and Suzanne Tamang. 2018. Synthnotes: A generator framework for high-volume, high-fidelity synthetic mental health notes. In 2018 IEEE international conference on big data (big data), pages 951–958. IEEE. 8.2 Annotation Guidelines and Clinical Responsibi… [find](https://scholar.google.com/scholar?q=Edmon+Begoli%2C+Kris+Brown%2C+Sudarshan+Srinivas%2C+and+Suzanne+Tamang%2E+2018%2E+Synthnotes%3A+A+generator+framework+for+high%2Dvolume%2C+high%2Dfidelity+synthetic+mental+health+notes%2E+In+2018+IEEE+international+confe) *Updated 1 November 2026*