Clinicalnerpt Healthcare

🚀 葡萄牙语临床命名实体识别 - 医疗保健

该医疗保健命名实体识别（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（与统一医学语言系统 UMLS 兼容）。所有来自 “pucpr” 用户的 NER 模型均基于巴西临床语料库 SemClinBr 进行训练，训练轮次为 10 次，采用 IOB2 格式，基础模型为 BioBERTpt(all)。

🚀 快速开始

该医疗保健 NER 模型是 BioBERTpt 项目 的一部分，此项目训练了 13 个临床实体模型（与 UMLS 兼容）。所有来自 “pucpr” 用户的 NER 模型都从巴西临床语料库 SemClinBr 进行训练，训练 10 个轮次，采用 IOB2 格式，基于 BioBERTpt(all) 模型。

👏 致谢

本研究部分由巴西高等教育人员素质提升协调局（CAPES）资助 - 资助代码 001。

📚 详细文档

引用信息

@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.",
}

❓ 常见问题

若有疑问，请在 BioBERTpt 仓库 上发布 GitHub 问题。