痴呆症护理研究和心理社会因素
Jiageng Wu1, Richard Wyss1, Josh Lin1
1Brigham and Women's Hospital, Boston, MA, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 26, 2025
概括
大型语言模型 (LLM) 可以使用电子健康记录 (EHR) 预测痴呆症患者的认知功能. 较大的LLM表现更好,显示出改善痴呆症护理可访问性和效率的潜力.
科学领域:
- 医疗保健中的人工智能
- 临床信息学 临床信息学
- 神经科学与衰老问题
背景情况:
- 认知功能对于痴呆症患者的生活质量至关重要.
- 准确的认知评估对于痴呆症管理至关重要.
- 传统的评估是资源密集型的,限制了可扩展性.
研究的目的:
- 评估大型语言模型 (LLM) 在预测痴呆患者认知功能方面的潜力.
- 利用电子健康记录 (EHR) 的非结构化数据进行认知评估.
- 为了比较各种LLM和推断策略的性能.
主要方法:
- 使用电子健康记录 (2013-2020) 的痴呆症患者 (≥65岁) 的回顾性队列研究.
- 通过心理状态简要访谈 (BIMS) 或M1700认知功能评估的认知功能.
- 多个LLM (例如QWEN2.5,Llama 3.3) 用标准,思维链 (CoT) 和基于摘要的推理策略进行了测试.
- 使用F1得分与启动程序评估的性能.
主要成果:
- 在LLM中,强大的跨数据集通用性得到了证明.
- 在OASIS评估中,Llama-3.3-70B获得了最高的F1得分 (66.31%),在MDS中获得了QWEN2.5-72B (45.23%).
- 较大的LLM提高了性能;CoT和摘要策略提高了可解释性,但不总是准确.
结论:
- 使用EHR数据,LLM显示出预测痴呆症患者的认知功能的巨大潜力.
- 较大的模型更有效,而先进的推理策略提高了可解释性.
- 临床文本需要进一步优化LLM策略,以提高可靠性和融入痴呆症护理工作流程.
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