提升对老年人认知能力下降的早期检测:在临床注释中使用大型语言模型进行比较研究
Xinsong Du1,2, John Novoa-Laurentiev1, Joseph M Plasaek1,2
1Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts 02115.
medRxiv : the preprint server for health sciences
|April 18, 2024
概括
大型语言模型 (LLM) 显示出从电子健康记录 (EHR) 中检测认知衰退的希望. 结合LLMs和传统方法的整体模型实现了高精度,优于单个模型.
科学领域:
- 医疗保健中的人工智能
- 临床信息学 临床信息学
背景情况:
- 评估大型语言模型 (LLM) 用于识别电子健康记录 (EHR) 中的认知衰退.
- 使用现实世界的临床笔记,将LLM绩效与传统模型进行比较.
- 解决LLM在特定临床状况检测中的不足探索的有效性.
结论:
- 在分析认知衰退的EHR数据时,LLM和传统模型表现出明显的错误模式.
- 集体建模有效地利用互补的优势,提高诊断准确度.
- 未来的工作应该集中在将LLM与本地化模型和特定领域的医学知识相结合.
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