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Bete: A Brazilian Portuguese Dataset for Named Entity Recognition and Relation Extraction in the Diabetes Healthcare Domain

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Intelligent Systems (BRACIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14197))

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Abstract

The biomedical NLP community has seen great advances in dataset development mostly for the English language, which has hindered progress in the field, as other languages are still underrepresented. This study introduces a dataset of Brazilian Portuguese annotated for named entity recognition and relation extraction in the healthcare domain. We compiled and annotated a corpus of health professionals’ responses to frequently asked questions in online healthcare forums on diabetes. We measured inter-annotator agreement and conducted initial experiments using up-to-date methods to recognize entities and extract relations, such as BERT-based ones. Data, models, and results are publicly available at https://github.com/pavalucas/Bete .

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Notes

  1. 1.

    Translation into English: “Being overweight can lead to type 2 diabetes. Therefore, intermittent fasting may be a way to prevent type 2 diabetes. Intermittent fasting can also be used as a treatment for people newly diagnosed with type 1 diabetes who need to lose weight to achieve a more stable health condition; these people should be advised and monitored by an endocrinologist and a nutritionist.”.

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Author information

Authors and Affiliations

  1. aiXplain Inc., Los Gatos, USA

    Lucas Pavanelli & Thiago Ferreira

  2. Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

    Yohan Bonescki Gumiel & Adriana Pagano

  3. Pontifícia Universidade Católica do Rio de Janeiro (PUC-RJ), Rio de Janeiro, Brazil

    Eduardo Laber

Authors
  1. Lucas Pavanelli
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  2. Yohan Bonescki Gumiel
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  3. Thiago Ferreira
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  4. Adriana Pagano
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  5. Eduardo Laber
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Corresponding author

Correspondence to Lucas Pavanelli .

Editor information

Editors and Affiliations

  1. Federal University of São Carlos, São Carlos, Brazil

    Murilo C. Naldi

  2. Centro Universitario da FEI, São Bernardo do Campo, Brazil

    Reinaldo A. C. Bianchi

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Ethical Statement

Our study fully complies with ethical standards and did not require any submission to ethical boards, since no data collection with human subjects was carried out. Our dataset was created by our team and contains texts drafted by medical students under the supervision of healthcare professionals, all of whom are research members in our project.

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Pavanelli, L., Gumiel, Y.B., Ferreira, T., Pagano, A., Laber, E. (2023). Bete: A Brazilian Portuguese Dataset for Named Entity Recognition and Relation Extraction in the Diabetes Healthcare Domain. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14197. Springer, Cham. https://doi.org/10.1007/978-3-031-45392-2_17

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  • DOI: https://doi.org/10.1007/978-3-031-45392-2_17

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