基于LLM的使用EHR的少数射击疾病预测:一种结合预测性代理推理和关键代理指令的新方法
Hejie Cui1,2, Zhuocheng Shen1, Jieyu Zhang3
1Department of Computer Science, Emory University, Atlanta, GA, USA.
概括
大型语言模型 (LLM) 可以将结构化的电子健康记录 (EHR) 转化为疾病预测的叙述. 这种方法对医疗保健应用具有前景,提供了竞争力的少数镜头性能.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 自然语言处理自然语言处理.
背景情况:
- 电子健康记录 (EHR) 是用于预测分析的丰富患者数据来源.
- 传统的EHR数据监督学习需要广泛的标记数据集,这给可访问性带来了挑战.
- 大型语言模型 (LLM) 为利用EHR数据提供了一个潜在的替代方案.
研究的目的:
- 探索使用LLM将结构化EHR数据转换为自然语言叙述的可行性.
- 评估基于EHR的预测任务的LLMs的零射击和少数射击性能.
- 引入一种新的LLM基于代理的方法,以提高预测准确性和推理.
主要方法:
- 结构化的患者访问数据 (诊断,实验室,处方) 用LLMs转换为自然语言叙述.
- 通过使用零射击和少数射击学习以及专门的提示策略来评估LLM绩效.
- 一个多代理的LLM系统,包括预测和批评代理商,是为了完善预测和推理而开发的.
主要成果:
- 从结构化EHR数据中生成自然语言叙述的能力.
- 拟议的LLM代理方法在疾病预测任务中实现了竞争性的少数射击性能.
- 结果表明,在特定的EHR预测场景中,LLM可以与传统监督方法相提并论.
结论:
- 在健康导向的应用中,LLM具有显著的潜力,特别是在使用EHR数据进行疾病预测方面.
- 该LLM代理框架为改善医疗保健预测模型的准确性和可解释性提供了一个有希望的方向.
- 这项研究强调了LLM作为从复杂的EHR数据中提取见解的工具的可行性.
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