在老年人中识别大型语言模型的抑郁机会:回顾性队列研究
Vimig Socrates1,2, Donald S Wright3,4, Thomas Huang3
1Department of Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, CT, United States.
JMIR aging
|April 11, 2025
概括
大型语言模型 (LLM) 在确定急诊室 (ED) 中老年人抑郁症的机会方面表现有前途,但在复杂的临床应用中扎. 为了准确,现实世界的使用,需要进一步开发.
科学领域:
- 人工智能在医学中的应用
- 老年人药理学 老年人药理学
- 紧急医疗 紧急医疗
背景情况:
- 老年人中的多种药物增加了不良药物事件,包括跌倒.
- 止处方旨在通过停止不适当的药物来减轻这些风险.
- 目前的抑郁症描述标准在时间有限的紧急情况下很难适用.
研究的目的:
- 评估一个大型语言模型 (LLM) 管道,以确定老年急诊室 (ED) 患者的抑郁症的机会.
- 使用Beers,STOPP和GEM推标准来评估LLM的表现.
- 检查LLM信心校准及其对推准确性的影响.
主要方法:
- 在美国一个学术医疗中心对100名老年人的回顾性队列研究ED.
- 在LLM管道中,我们过了减记标准,并使用患者数据将其应用.
- 模型建议与医学学生相比较,ED医生判断的差异.
主要成果:
- 与医学学生相比,LLM有效地确定了抑郁的标准 (PPV 0.83,NPV 0.93).
- 由于复杂的标准和缺少的数据,LLM在特定的减值建议 (PPV 0.47,NPV 0.93) 中遇到了困难.
- 选择性预测显示,由于对LLM信心的校准不佳,改善程度有限.
结论:
- LLM可以帮助过ED使用的描述标准.
- 在将标准应用于复杂的临床场景并确保准确的LLM信心方面,仍然存在挑战.
- 改进的指导方针,LLM校准和人类-AI集成对于有效的临床实施至关重要.
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