从使用大型语言模型的护理报告中识别 Delirium
Lisa Graf1,2, Alexander Ritzi3,4, Lili M Schoeler3,5
1Neurorobotics Lab, Department of Computer Science - University of Freiburg, Germany.
Studies in health technology and informatics
|May 17, 2025
概括
这项研究探讨了使用大型语言模型来检测护理报告中的妄想症. 微调 Phi3 模型实现了最高的准确性,显著优于其他方法来完成这一关键的临床任务.
科学领域:
- 人工智能的人工智能
- 临床信息学 临床信息学
- 自然语言处理自然语言处理.
背景情况:
- 从非结构化的护理报告中检测 Delirium 是一个挑战.
- 需要自动化方法来提高准确性和效率.
- 大型语言模型 (LLM) 显示出临床文本分析的前景.
研究的目的:
- 在护理笔记中评估LLM用于错觉检测.
- 为了比较关键字匹配,提示和微调方法.
- 为此任务确定最有效的LLM策略.
主要方法:
- 使用来自德国弗赖堡大学医院的手动标记数据集.
- 测试了Llama3和Phi3的大型语言模型.
- 在关键字匹配,提示和微调技术中比较性能.
主要成果:
- 提示和微调LLM都对错觉检测有效.
- 微调 Phi3 (3.8B) 模型的精度是最高的 (90.24%).
- 菲3微调也获得了最好的AUROC (96.07%),优于其他方法.
结论:
- LLM,特别是像 Phi3 这样的微调模型,对于自动化妄检测非常有效.
- 微调提供了比提示和关键字匹配更高的性能.
- 这种方法可以通过更好地识别妄想来加强临床决策和患者护理.
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