Clinicalnerpt Healthcare

🚀 葡萄牙語臨床命名實體識別 - 醫療保健

該醫療保健命名實體識別（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（與統一醫學語言系統 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，訓練輪次為 10 次，採用 IOB2 格式，基礎模型為 BioBERTpt(all)。

🚀 快速開始

該醫療保健 NER 模型是 BioBERTpt 項目 的一部分，此項目訓練了 13 個臨床實體模型（與 UMLS 兼容）。所有來自 “pucpr” 用戶的 NER 模型都從巴西臨床語料庫 SemClinBr 進行訓練，訓練 10 個輪次，採用 IOB2 格式，基於 BioBERTpt(all) 模型。

👏 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 001。

📚 詳細文檔

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.",
}

❓ 常見問題

若有疑問，請在 BioBERTpt 倉庫 上發佈 GitHub 問題。