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Updated: May 11, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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用自然语言处理技术和在重症监护室环境中的开放数据来预测扩展频谱β-乳糖酶产生细菌感染的预测模型:回顾性观察性研究

Genta Ito1, Shuntaro Yada1, Shoko Wakamiya1

  • 1Department of Information Science, Nara Institute of Science and Technology, Ikoma City, Japan.

JMIR formative research
|July 10, 2024
PubMed
概括

机器学习模型可以使用电子健康记录数据预测产生扩展光谱β-乳糖酶 (ESBL) 的细菌感染. 整合文本信息显著提高了预测准确性,证明了开放数据和NLP工具的价值.

关键词:
产生ESBL的细菌感染.在MIMIC-3数据集中.在NLP中,我们使用了NLP.快速的UMLS 快速的UMLS命名实体的认可 命名实体的认可自然语言处理自然语言处理.预测建模预测建模

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