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语言差异对使用深度学习的住院患者倒检测的影响.

Insook Cho1, EunJu Lee1, Dong-Geon Lee2

  • 1Nursing Department, Inha University, Incheon, Republic of Korea.

Studies in health technology and informatics
|March 1, 2024
PubMed
概括

这项研究表明,语言差异对自然语言处理 (NLP) 在护理笔记中识别患者跌倒的影响. 在韩语和英语文本中,NLP模型的性能不同,这影响了落检测的准确性.

关键词:
住院患者跌倒,跌倒.深度学习是一种深度学习.事件检测事件检测事件检测护理笔记 护理笔记文本数据 文本数据

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科学领域:

  • 临床信息学 临床信息学
  • 自然语言处理自然语言处理.
  • 医疗保健数据分析数据分析

背景情况:

  • 非结构化的护理笔记包含有价值的临床信息.
  • 准确识别患者的跌倒对于患者的安全和质量改善至关重要.
  • 自然语言处理 (NLP) 提供了从文本中提取临床事件的潜力.

研究的目的:

  • 调查韩语和英语之间的语言差异对NLP表现的影响.
  • 为了评估NLP在分类住院患者摔倒中的有效性,从不同语言的护理笔记.
  • 确定跨语言NLP在医疗保健中的挑战和机遇.

主要方法:

  • 利用了韩语和英语非结构化护理笔记的数据集.
  • 应用NLP技术进行文本分类,以识别住院患者跌倒的情况.
  • 在两种语言中比较NLP模型的性能指标.

主要成果:

  • 在韩国和英语之间观察到NLP表现的显著差异.
  • 每种语言独特的语言特征为识别落提出了独特的挑战.
  • 模型的准确性各不相同,这表明可能需要对特定语言进行优化.

结论:

  • 语言差异明显影响了NLP在临床文本分析中的表现.
  • 跨语言的NLP医疗保健需要仔细考虑语言细微差别.
  • 需要进一步的研究来开发针对患者安全事件的强大,语言无关的NLP解决方案.