使用大型语言模型进行认知问题的临床检测的自主代理工作流
Jiazi Tian1, Pedram Fard1, Cameron Cagan1
1Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
NPJ digital medicine
|January 7, 2026
概括
我们开发了人工智能工作流程,从临床笔记中检测认知障碍. 自主人工智能代理实现了专家级别的性能,为早期检测提供可扩展的临床决策支持.
科学领域:
- 人工智能的人工智能
- 临床信息学 临床信息学
- 认知科学 认知科学
背景情况:
- 传统的认知障碍查在可访问性和资源分配方面面临限制.
- 早期检测对于及时干预和管理认知衰退至关重要.
- 大型语言模型 (LLM) 提供了分析临床笔记以确定认知问题的潜力.
研究的目的:
- 开发和比较两种LLM工作流程,用于在临床笔记中识别认知问题.
- 评估专家驱动的工作流与自主代理工作流的性能.
- 评估流行率转移对模型通用性和差异临床相关性的影响.
主要方法:
- 开发了一个以专家为导向的LLM工作流程,使用LLaMA 3.1,LLaMA 3.2,Med42 v2.2.的代快速改进.
- 创建了一个自主代理工作流程,其中有五个专门的代理来进行快速优化.
- 在平衡数据集上优化模型,并在反映现实世界的流行率的独立数据集上验证.
- 进行专家重新裁定,以分析假阴性和评估临床推理.
主要成果:
- 自主代理工作流实现了与专家驱动工作流 (F1=0.81) 相当的验证性能 (F1=0.74).
- 与专家驱动的工作流 (0.87) 相比,代理工作流显示出更高的精细化结果 (0.93).
- 由于流行率的变化,改进和验证数据集之间的模型灵敏度从0.91下降到0.62.
- 专家重新判定发现44%的明显假阴性代表了临床上适当的推理.
结论:
- 自主代理系统可以从临床笔记中识别认知问题的专家级性能.
- 这些人工智能系统为早期认知障碍检测提供可扩展和可解释的临床决策支持.
- 数据流行率的变化对模型的概括性产生了重大影响,需要谨慎的验证策略.
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