CARE-SD:基于分类器的分析,以识别电子健康记录中的提供者污名化和怀疑标记标签:模型开发和验证
Andrew Walker1, Annie Thorne2, Sudeshna Das1
1Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, United States.
Journal of the American Medical Informatics Association : JAMIA
|December 26, 2024
概括
这项研究开发了自然语言处理分类器,以检测重症监护电子健康记录中的污名化语言和怀疑标记,显示出识别偏见术语的高性能.
科学领域:
- 自然语言处理自然语言处理.
- 临床信息学 临床信息学
- 公共卫生 公共卫生
背景情况:
- 电子健康记录 (EHR) 包含敏感的患者信息.
- 识别EHR中的污名化和偏见的语言对于公平的护理至关重要.
- 以前用于检测这种语言的方法是有限的.
研究的目的:
- 开发和验证自然语言处理 (NLP) 技术,以检测在重症监护室 (ICU) 的污名化和偏见语言.
- 创建一个词典的污名化标签,怀疑标记,和恐惧引文.
- 培训监督学习分类器,以自动识别这些语言特征.
主要方法:
- 使用文献,Word2Vec和GPT 3.5开发了词典,通过人类评估进行了改进.
- 从MIMIC-III数据集中搜索了1800万个句子,以寻找语言偏差特征.
- 采集样本,由专家进行注释,并为监督学习分类器每项特征使用1000个句子.
主要成果:
- 创建了58个怀疑标记和127个耻辱标签表达式的词典.
- 在怀疑标记和耻辱标签分类器 (宏观F1分数分别为0.84和0.79) 中取得了高性能.
- 分类器的准确性与人类注释器的准确性非常接近 (0.87).
结论:
- 已证明监督分类器的可行性,用于识别医疗文本中的耻辱标签和怀疑标记.
- 确定了在电子人权中对语言使用的耻辱化趋势.
- 可以应用分类器来减少医疗保健系统中偏见的语言.
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