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相关实验视频

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临床表现 临床表现

Liqin Wang1, Rebecca E Amariglio2, Li Zhou1

  • 1Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

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|December 26, 2025
PubMed
概括

大型语言模型 (LLM) 可以在电子健康记录中识别主观认知衰退 (SCD). 这项研究强调了SCD患者常见的认知问题,例如SCD患者的记忆问题.

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科学领域:

  • 神经学 神经学
  • 人工智能的人工智能
  • 医疗信息学 医疗信息学

背景情况:

  • 主观认知衰退 (SCD) 代表早期记忆或认知问题,尽管正常的测试表现,对于早期干预至关重要.
  • 由于缺乏标准化代码和依赖自由文本电子健康记录 (EHR),SCD经常被遗漏.
  • 有限的研究存在于SCD患者的特征和记录的认知问题.

研究的目的:

  • 调查使用大型语言模型 (LLM) 来识别和描述疑似SCD患者认知问题的可行性.
  • 对具有正常认知测试分数的患者在电子健康记录中记录的认知问题的常见主题和子主题进行分析.

主要方法:

  • 追溯队列研究分析了来自Mass General Brigham (MGB) 的EHR数据,用于65岁以上初步认知评估的患者.
  • 采用双层LLM方法 (Med42-v2-8B和GPT-4o) 来识别临床笔记中的认知问题.
  • GPT-4o被用来总结主题和子主题,通过比较患者组的统计分析.

主要成果:

  • 该研究包括15,750名患者;13.8% (2,175) 发现认知问题或SCD.
  • 课程确定了12个主要主题和34个认知问题的子主题.
  • 最常见的主题包括记忆问题 (69.7%),认知处理/速度 (26.5%),困惑/迷失方向 (25.3%) 和语言问题 (23.8%).

结论:

  • 在被认为患有SCD的患者中,LLM有效地提取和分类认知问题.
  • 确定的主题为早期认知问题的性质提供了有价值的见解.
  • 未来的研究应该将这些症状与痴呆症进展联系起来.