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Documentation of Nursing Diagnosis01:10

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The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
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Updated: Jul 5, 2025

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
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有用的错误:自动语音识别错误可以改善下游痴呆症分类吗?

Changye Li1, Weizhe Xu2, Trevor Cohen2

  • 1Institute of Health Informatics, University of Minnesota, Minneapolis, 55455, MN, USA.

Journal of biomedical informatics
|January 22, 2024
PubMed
概括
此摘要是机器生成的。

痴呆症分类中的自动语音识别 (ASR) 错误令人惊地提高了阿尔茨海默病检测的准确性. 不完美的ASR转录为认知障碍评估提供了有价值的语言线索.

关键词:
自动语音识别自动语音识别痴呆症是一种痴呆症.可解释的人工智能自然语言处理自然语言处理.

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科学领域:

  • 计算语言学计算语言学
  • 神经科学是一个神经科学.
  • 医疗保健中的人工智能

背景情况:

  • 阿尔茨海默病 (AD) 诊断依赖于认知评估,通常涉及语言分析.
  • 自动语音识别 (ASR) 系统越来越多地用于转录口语.

研究的目的:

  • 调查ASR错误对痴呆症分类准确性的影响.
  • 确定不完美的ASR转录是否可以帮助区分阿尔茨海默病与健康认知.
  • 为了评估ASR生成的转录在"Cookie Theft"图片描述任务中的有效性.

主要方法:

  • 实验中使用了各种ASR模型,并进行后编辑以改进转录.
  • 不完美的ASR和手册成绩单都作为痴呆症分类模型的输入.
  • 进行了全面的错误分析,以比较模型性能.

主要成果:

  • 不完善的ASR转录在对阿尔茨海默病的分类中表现优于手册转录.
  • 基于ASR的模型超过了以前的最先进的性能.
  • 发现ASR错误包含了与痴呆相关的有价值的语言线索.

结论:

  • 不完美的ASR转录捕捉了与痴呆症相关的语言异常,提高了分类准确性.
  • ASR和分类模型之间的协同作用突显了ASR在认知障碍评估中的潜力.
  • 作为评估认知健康的临床应用工具,ASR显示出前景.