利用大型语言模型来检测患者-呼吸机异步.
Francesc Suñol1, Candelaria de Haro2,3, Verónica Santos-Pulpón4
1Institut d'Investigació i Innovació Parc Taulí (I3PT-CERCA), Sabadell, Spain fxsunol@tauli.cat.
BMJ health & care informatics
|June 27, 2025
概括
大型语言模型 (LLM) 在检测流量饥饿 (FS) 异步方面显示有限的直接准确性,但在为此临床任务生成有效的深度学习模型方面表现出色.
科学领域:
- 医疗人工智能 医疗人工智能
- 临床决策支持系统 临床决策支持系统
- 呼吸机械学 呼吸机械
背景情况:
- 患者-呼吸机异步,特别是流量饥饿 (FS),是机械通风的一个关键问题.
- 精确检测FS对于优化患者的治疗结果和呼吸机管理至关重要.
- 目前用于FS检测的方法通常依赖于复杂的,专家开发的模型.
研究的目的:
- 评估大型语言模型 (LLM) 在检测流量饥饿 (FS) 异步方面的性能.
- 将LLM的表现与专家开发的深度神经网络进行比较.
- 评估LLM在FS检测中使用深度学习模型生成代码的能力.
主要方法:
- 四种流行的LLM (GPT-4,Claude-3.5,Gemini-1.5,DeepSeek-R1) 在6500个气道压力周期中进行了测试.
- 在LLM中,呼吸被直接分为三个FS类别.
- LLM为一维卷积神经网络 (CNN-1D) 和长短期记忆 (LSTM) 模型生成了可执行代码.
主要成果:
- 在直接FS分类中,LLM表现不佳,准确度从0.497到0.627.7不等.
- 克劳德3.5生成的CNN-1D代码实现了0.902的高精度,超过了专家开发的模型.
- 在临床应用的自动化模型开发中,LLM显示出显著的潜力.
结论:
- 对于FS分类来说,LLM的直接实用性有限,但在生成深度学习模型方面非常有效.
- 通过LLM加速临床工具的开发,例如用于检测患者-呼吸机异步,这种能力是有希望的.
- 在临床实施LLM生成模型之前,需要进一步验证和伦理考虑.
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