Clinicalnerpt Pharmacologic

🚀 Portuguese Clinical NER - Pharmacologic

This Pharmacologic NER model is part of the BioBERTpt project. In this project, 13 clinical entity models (compatible with UMLS) were trained. All NER models from the "pucpr" user were trained on the Brazilian clinical corpus SemClinBr for 10 epochs in the IOB2 format, starting from the BioBERTpt(all) model.

✨ Features

• The model is trained on the Brazilian clinical corpus SemClinBr, which is highly relevant to the Portuguese - speaking clinical context.
• It is part of a set of 13 clinical entity models, providing comprehensive support for clinical NLP tasks.

📦 Installation

No installation steps were provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

The widget examples show how the model can be applied in real - world scenarios:

• "COMO ESQUEMA DE MEDICAÇÃO PARA ICC PRESCRITO NO ALTA, RECEBE FUROSEMIDA 40 BID, ISOSSORBIDA 40 TID, DIGOXINA 0,25 /D, CAPTOPRIL 50 TID E ESPIRONOLACTONA 25 /D."
• "ESTAVA EM USO DE FUROSEMIDA 40 BID, DIGOXINA 0,25 /D, SINVASTATINA 40 /NOITE, CAPTOPRIL 50 TID, ISOSSORBIDA 20 TID, AAS 100 /D E ESPIRONOLACTONA 25 /D."

📚 Documentation

Acknowledgements

This study was partly funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

Citation

@inproceedings{schneider-etal-2020-biobertpt,
    title = "{B}io{BERT}pt - A {P}ortuguese Neural Language Model for Clinical Named Entity Recognition",
    author = "Schneider, Elisa Terumi Rubel  and
      de Souza, Jo{\~a}o Vitor Andrioli  and
      Knafou, Julien  and
      Oliveira, Lucas Emanuel Silva e  and
      Copara, Jenny  and
      Gumiel, Yohan Bonescki  and
      Oliveira, Lucas Ferro Antunes de  and
      Paraiso, Emerson Cabrera  and
      Teodoro, Douglas  and
      Barra, Cl{\'a}udia Maria Cabral Moro",
    booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.7",
    pages = "65--72",
    abstract = "With the growing number of electronic health record data, clinical NLP tasks have become increasingly relevant to unlock valuable information from unstructured clinical text. Although the performance of downstream NLP tasks, such as named-entity recognition (NER), in English corpus has recently improved by contextualised language models, less research is available for clinical texts in low resource languages. Our goal is to assess a deep contextual embedding model for Portuguese, so called BioBERTpt, to support clinical and biomedical NER. We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese. To evaluate the performance of BioBERTpt, we ran NER experiments on two annotated corpora containing clinical narratives and compared the results with existing BERT models. Our in-domain model outperformed the baseline model in F1-score by 2.72{\%}, achieving higher performance in 11 out of 13 assessed entities. We demonstrate that enriching contextual embedding models with domain literature can play an important role in improving performance for specific NLP tasks. The transfer learning process enhanced the Portuguese biomedical NER model by reducing the necessity of labeled data and the demand for retraining a whole new model.",
}

Questions?

Post a Github issue on the BioBERTpt repo.