🚀 葡萄牙语临床命名实体识别 - 发现模型

该发现命名实体识别（NER）模型是BioBERTpt项目的一部分，该项目训练了13个临床实体模型（与UMLS兼容）。此模型为临床信息挖掘提供了有力支持。

🚀 快速开始

发现命名实体识别模型是BioBERTpt项目的一部分，该项目训练了13个临床实体模型（与UMLS兼容）。所有来自“pucpr”用户的命名实体识别模型均基于巴西临床语料库SemClinBr进行训练，训练轮数为10轮，采用IOB2格式，基于BioBERTpt（全量）模型。

📚 详细文档

数据集示例

• "RECEBE ALTA EM BOM ESTADO GERAL, COM PLANO DE ACOMPANHAR NO AMBULATÓRIO."
• "PACIENTE APRESENTOU BOA EVOLUÇÃO CLÍNICA APÓS OTIMIZAÇÃO DO TTO DA ICC."

数据集

• SemClinBr

🙏 致谢

本研究部分由巴西高等教育人员发展协调局（CAPES）资助 - 资助代码001。

📄 引用

@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.",
}

❓ 问题反馈

若有任何问题，请在BioBERTpt仓库提交GitHub问题。