Clinicalnerpt Therapeutic

🚀 ポルトガル語臨床固有表現抽出 - 治療分野

この治療分野の固有表現抽出（NER）モデルは、BioBERTptプロジェクト の一部です。このプロジェクトでは、13種類の臨床エンティティモデル（統一医学言語システムUMLSと互換性があります）が学習されています。「pucpr」ユーザーによるすべてのNERモデルは、ブラジルの臨床コーパス SemClinBr を基に、BioBERTpt（全量）モデルを起点として、10エポックの学習を行い、IOB2形式で訓練されています。

サンプルテキスト

• “Dispneia venoso central em subclavia D duplolumen recebendo solução salina e glicosada em BI.”
• “Paciente com Sepse pulmonar em D8 tazocin (paciente não recebeu por 2 dias Atb).”
• “FOI REALIZADO CURSO DE ATB COM LEVOFLOXACINA POR 7 DIAS.”

データセット

• SemClinBr

プロジェクトアイコン

📚 詳細ドキュメント

謝辞

本研究の一部は、ブラジル高等教育人員開発協調局（CAPES）によって助成されており、助成コードは001です。

引用情報

@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.",
}

問題フィードバック

何か問題があれば、BioBERTptリポジトリ でGitHub issueを提出してください。