使用自然语言处理来发现产科文档中的种族偏见
Itamar D Futterman1, Hila Friedmann2, Oleksii Shpanel-Yukhta2
1Department of Obstetrics and Gynecology, Division of Maternal Fetal Medicine, Maimonides Medical Center, Brooklyn, NY, United States of America.
Clinical imaging
|May 1, 2024
概括
自然语言处理 (NLP) 在怀孕文档中发现了纤维化子宫的差异. 黑人患者经历了延迟或缺失的诊断条目,突出了护理质量的潜在不平等.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床数据分析 临床数据分析
背景情况:
- 临床文档对于患者护理和研究至关重要.
- 将自由文本临床注释整合到数据分析中存在挑战.
- 自然语言处理 (NLP) 为分析非结构化临床数据提供了一个解决方案.
研究的目的:
- 评估NLP在识别临床文档缺口方面的有效性.
- 为了将NLP衍生数据与传统的ICD10代码进行比较,用于在怀孕期间诊断子宫纤维瘤.
- 评估这种情况的文档中的种族差异.
主要方法:
- 通过使用出生数据进行了横截面研究.
- 在怀孕期间 (>5厘米) 经放射学证实的子宫纤维瘤病例被使用NLP平台确定.
- 将NLP数据与非NLP衍生的数据 (ICD10代码) 进行比较,并按种族分层.
主要成果:
- 在怀孕期间,NLP成功发现了子宫纤维瘤的病例.
- 发现黑人患者更有可能迟到或缺少诊断文档.
- 识别了文档上的差距,并按种族分层.
结论:
- 在临床环境中,NLP是检测文档缺陷的宝贵工具.
- 识别文档缺口可以提高患者护理质量.
- 解决文档中的种族差异对于公平的医疗保健至关重要.
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