结构化临床方法使得大型语言模型能够用于改进临床诊断和可解释的推理
Muhammad Ayoub1,2, Hai Zhao3,4, Lifeng Li5
1AGI Institute, School of Computer Science, Shanghai Jiaotong University, Shanghai, Shanghai, China.
一个新的临床框架增强了大型语言模型 (LLM) 的诊断推理,提高了准确性,并为医疗应用提供了可解释的输出. 这种方法可以确保在各种数据集中提供可靠的决策支持.
科学领域:
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
- 自然语言处理自然语言处理.
背景情况:
- 信任和泛化挑战阻碍了大型语言模型 (LLM) 在医学诊断中的应用.
- 现有的LLM诊断方法在各种疾病和患者数据中缺乏稳定性.
- 需要一个通用的框架来提高诊断性能和可解释的推理.
研究的目的:
- 开发一个通用的,基于模型的临床框架,以提高诊断准确度.
- 将可解释的推理纳入LLM驱动的诊断过程中.
- 在各种医疗数据集和模型中改进LLM的泛化.
主要方法:
- 结构化临床方法模拟现实世界诊断工作流程.
- 将患者叙述转化为标记的临床组件.
- 模型诊断的验证使用疾病知识算法和逐步决策模拟.
- 跨多个LLM和临床推理数据集的评估.
主要成果:
- 在六个LLM和两个临床数据集中表现优于现有的提示方法.
- 获得高的诊断F1分数 (0.93-0.95) 与最小的错误分类.
- 在电子健康记录数据上证明有效,可解释的临床结果,准确度高 (0.91).
结论:
- 开发的框架是强大的,可以在各种模型和数据集中进行概括.
- 可靠,可扩展和可解释的临床决策支持的潜力.
- 证实了在现实世界临床环境中的适用性.
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