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通过上下文学习有效检测电子健康记录中的污名化语言:比较分析和验证研究

Hongbo Chen¹, Myrtede Alfred¹, Eldan Cohen¹

¹Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.

JMIR medical informatics

|August 18, 2025

概括

在语境学习 (ICL) 有效地检测到电子健康记录 (EHR) 中的污名化语言,数据较少. 与传统方法相比,这种数据效率高的方法也显示出更好的公平性.

科学领域:

自然语言处理 (NLP) 是一种自然语言处理.
医疗保健中的机器学习
临床信息学临床信息学

背景情况:

电子健康记录 (EHR) 中的污名化语言延续了偏见,并危及了患者的护理.
监督的机器学习模型用于耻辱检测需要广泛的注释数据集.
在上下文学习 (ICL) 提供了使用指令和示例的数据效率替代方案.

研究的目的:

评估ICL在电子健康记录中检测污名化语言的有效性.
在数据稀缺条件下评估ICL性能.
将ICL与传统的机器学习方法在效率和公平性方面进行比较.

主要方法:

从密集护理-IV (MIMIC-IV) 数据集中的医疗信息中心分析了5043个句子.
对比ICL (零射击,少数射击与4个提示策略) 与零射击文本包含和少数射击SetFit模型.
使用跨受保护属性 (性别,年龄,种族) 的平等绩效标准评估模型公平性.

主要成果:

像GEMMA-2 (零射击) 和LLAMA-3 (几射击) 这样的ICL模型在检测污名化语言方面明显优于文本包容和SetFit模型.
最好的ICL模型在仅有32个标记实例的情况下获得了0.901的F1得分,与在数千个实例上训练的监督微调模型密切竞争.
与监督微调模型相比,ICL模型表现出更高的公平性,在受保护属性中表现出更少的偏差.

结论:

ICL提供了一种灵活而稳健的方法,用于识别电子健康文件中的污名化语言.
ICL是一个比传统的监督机器学习更有效,更公平的数据替代方案.
ICL可以改善临床文档中的偏差检测,同时最大限度地减少对大型标记数据集的需求.

关键词:

人工智能的人工智能是人工智能.电子健康记录电子健康记录公平的公平的公平.几次射击,几次射击.在上下文学习学习.大型语言模型机器学习是机器学习.促使战略促使战略.污名化的语言.文字分类文本分类文本分类没有射击的零射击.

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