Clinicalnerpt Pharmacologic

🚀 葡萄牙语临床命名实体识别 - 药理学

本药理学命名实体识别（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）。所有以 "pucpr" 用户命名的 NER 模型均基于巴西临床语料库 SemClinBr 进行训练，以 BioBERTpt(all) 模型为基础，训练 10 个轮次，并采用 IOB2 格式。

示例文本

• “作为出院时开具的心力衰竭药物治疗方案，患者接受呋塞米 40mg，每日两次；异山梨酯 40mg，每日三次；地高辛 0.25mg/天；卡托普利 50mg，每日三次；螺内酯 25mg/天。”
• “患者正在使用呋塞米 40mg，每日两次；地高辛 0.25mg/天；辛伐他汀 40mg/晚；卡托普利 50mg，每日三次；异山梨酯 20mg，每日三次；阿司匹林 100mg/天；螺内酯 25mg/天。”

📚 详细文档

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。

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.",
}

常见问题

若有任何问题，请在 BioBERTpt 代码库 中提交 GitHub 问题。