大型语言模型揭示了发作结果的差异
Kevin Xie1,2, William K S Ojemann1,2, Ryan S Gallagher2,3
1University of Pennsylvania, Dept. of Bioengineering, Philadelphia, PA, USA.
medRxiv : the preprint server for health sciences
|October 4, 2023
概括
大语言模型 (LLM) 显示在治疗中没有偏差. 然而,LLM分析显示,女性,公共保险患者和低收入地区的患者的发作结果更差,突出了护理差异.
科学领域:
- 医疗信息学 医疗信息学
- 神经学 神经学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 医疗保健中的大语言模型 (LLM) 可能会延续或引入偏见.
- 健康的社会决定因素会影响患者获得护理,但它们对获得专业护理的人群中发作结果的影响尚不清楚.
研究的目的:
- 评估一个经过验证的,特异性的LLM内在偏见.
- 使用LLM提取的数据调查是否存在人口差异的扣押结果.
主要方法:
- 用手动注释的笔记测试了LLM的种族,种族,性别,收入和保险方面的偏见.
- 从84,675次诊所访问中分析了LLM分类的发作自由度 (25,612名患者,2005-2022),以发现结果的差异.
主要成果:
- 在分类表现中,在人口群体之间没有发现显著的LLM偏差.
- 多变量分析显示,女性,公共保险持有人和低收入人士的发作结果更差.
- 与白人患者相比,黑人患者在单变量但不是多变量分析中表现较差.
结论:
- 通过LLM提取的发作数据显示了治疗结果的显著差异.
- 研究结果强调,迫切需要解决和减少管理中的健康差异.
- 确保所有患者得到公平的护理仍然是一个关键的挑战.
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