Open Dataset
Data Structure ?
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Data Structure ?
*The above analysis is the result extracted and analyzed by the system, and the specific actual data shall prevail.
README.md
問題の説明
臨床研究では、臨床記述に詳細に記録された患者の情報がしばしば必要とされます。固有表現抽出(NER)は、臨床記述から関心のあるエンティティ(例えば、疾患名、薬品名、検査名など)を抽出するための基本的な自然言語処理(NLP)タスクであり、臨床研究やトランスレーショナルリサーチをサポートするために行われます。臨床ノートは、豊富で詳細な医療情報を描き出しているため、臨床研究やその他の医療業務に重要な情報を引き出すために、より詳細に分析されています。
このチャレンジでは、トレーニングセットの30000の文書に対するラベル付けされたエンティティ(疾患)が与えられた上で、ハッカーに対して、テストセットに含まれる20000の段落/文書からすべての疾患名を抽出することを求めます。
例えば、以下は臨床報告の文です。
我々は、単一点レーザードップラー血流計(LDF)とレーザースペックルコントラスト解析(LSCI)によって評価された閉塞後反応性充血(PORH)の日間再現性を比較しました。
与えられた文では、反応性充血(太字)が疾患/適応症というタイプの固有表現です。
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- 1. The data originates from internet data collection or provided by service providers, and this platform offers users the ability to view and browse datasets.
- 2. This platform serves only as a basic information display for datasets, including but not limited to image, text, video, and audio file types.
- 3. Basic dataset information comes from the original data source or the information provided by the data provider. If there are discrepancies in the dataset description, please refer to the original data source or service provider's address.
- 1. All datasets on this site are copyrighted by their original publishers or data providers.
- 1. If you need to repost data from this site, please retain the original data source URL and related copyright notices.
- 1. If any data on this site involves infringement, please contact us promptly, and we will arrange for the data to be taken offline.