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临床表型的零射击学习:比较LLM和基于规则的方法.

Bernardo Neves1, José Maria Moreira2, Simão Gonçalves2

  • 1Hospital da Luz Learning Health, Luz Saúde, Lisboa, Portugal; Internal Medicine Department, Hospital da Luz Lisboa, Lisboa, Portugal; INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Portugal; Católica Medical School, Universidade Católica Portuguesa, Portugal.

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大型语言模型 (LLM) 能够从电子健康记录 (EHR) 中有效地实现慢性疾病的零射击表型. GPT-4o表现出卓越的性能,减少了数据科学应用程序的手动注释需求.

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大型语言模型.多种疾病多重症.现型化 (Phenotyping) 是一种表现方式.零射击学习的学习.

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科学领域:

  • 计算健康信息学 医疗信息学
  • 人工智能在医学中的应用
  • 数据科学用于医疗保健

背景情况:

  • 表型,在临床数据中分类疾病,对于电子健康记录 (EHR) 数据科学至关重要.
  • 传统的表型化方法是劳动密集型的,难以扩展.
  • 自动化表型化对于利用大型EHR数据集至关重要.

研究的目的:

  • 评估大型语言模型 (LLM) 用于慢性疾病的零射击表型化.
  • 将LLM的绩效与传统的基于规则的方法进行比较.
  • 在EHR数据中评估基于LLM的表型化的效率和准确性.

主要方法:

  • 研究了20种慢性疾病的零射击表型,使用来自EHR的合成患者摘要.
  • 评估了多种LLM (GPT-4o,GPT-3.5,LLaMA 3) 和基于规则的方法.
  • 利用来自里斯本医院1000名患者的数据集进行分析.

主要成果:

  • GPT-4o获得了最高的回忆率 (0.97) 和宏观F1得分 (0.92),优于其他LLM和基于规则的方法.
  • 基于规则的方法显示高精度 (0.92),但回忆率较低 (0.36).
  • 将基于规则的方法与LLM集成,通过集中人工努力,提高了整体表型精度.

结论:

  • 使用LLM的零射击学习,特别是GPT-4o,为EHR表型化提供了一种高效和准确的方法.
  • LLM显著减少了对广泛标记数据集的要求.
  • 这种方法提高了慢性疾病表型的准确性和可解释性.