使用LLM从EHR中获取证据:可能性和挑战
Hiba Ahsan1, Denis Jered McInerney1, Jisoo Kim2
1Northeastern University, Boston, MA.
Proceedings of machine learning research
|September 3, 2024
概括
大型语言模型 (LLM) 可以有效地检索和总结放射科医生的电子健康记录 (EHR) 中的关键信息. 对其输出的LLM信心与准确的摘要相关,减轻了潜在的混.
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
- 放射学 放射学是一门学科.
背景情况:
- 电子健康记录 (EHR) 中的非结构化数据包含了重要的诊断信息,由于手动审查的限制,经常被忽视.
- 放射科医生面临时间的限制,这使得从广泛的患者笔记中提取相关证据变得具有挑战性.
研究的目的:
- 提出和评估使用大型语言模型 (LLM) 进行零射击策略,以高效地检索和总结非结构化EHR数据.
- 评估LLM在识别和总结患者病情以及从临床笔记中支持证据方面的有效性.
主要方法:
- 开发了一种零射击的LLM策略,以推断患者的病情,并从EHR笔记中总结支持证据.
- 通过专家评估,LLM生成的输出与LLM前的信息检索基线进行了比较.
- 提出并验证了一种基于LLM的评估方法,以扩大LLM成果的评估规模.
主要成果:
- 基于LLM的检索和总结始终被专家评估者比基线方法更喜欢.
- 通过LLM生成的输出显示了模型信心和总结证据的可信度之间的强烈相关性.
- 基于LLM的评估方法在扩大评估过程中被证明是有效的.
结论:
- 作为接口,LLM显示出显著的希望,用于访问和解释EHR中的非结构化数据,以支持临床决策.
- 虽然LLM法学士学位.
- 幻觉 幻觉 幻觉 幻觉 幻觉
- 尽管模型的准确性仍然是一个挑战,但模型的信心可以作为一个实际的指标,以确保总结的准确性.
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