Clinicalnerpt Procedure

🚀 葡萄牙語臨床命名實體識別 - 程序

本程序命名實體識別（NER）模型是 BioBERTpt項目 的一部分，該項目訓練了13個臨床實體模型（與UMLS兼容）。所有來自“pucpr”用戶的NER模型均基於巴西臨床語料庫 SemClinBr 進行訓練，從BioBERTpt(all)模型開始，訓練10個輪次，並採用IOB2格式。

示例文本

• “Dispneia venoso central em subclavia D duplolumen recebendo solução salina e glicosada em BI.”
• “FOI REALIZADO CURSO DE ATB COM LEVOFLOXACINA POR 7 DIAS.”

數據集

• SemClinBr

項目縮略圖

📚 詳細文檔

致謝

本研究部分由巴西高等教育人員素質提升協調局（CAPES）資助，資助代碼為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.",
}

問題反饋

若有任何問題，請在 BioBERTpt倉庫 上提交GitHub issue。