审计深度学习中的学习协会从临床文本中提取种族和种族的方法
Oliver J Bear Don't Walk Iv1, Adrienne Pichon2, Harry Reyes Nieva2,3
1University of Washington, Seattle, WA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|January 15, 2024
概括
准确的种族和民族 (RE) 数据对于健康公平研究至关重要. 本研究审计了用于RE识别的NLP模型,揭示了可能对患者造成伤害的偏见,如果不加以解决.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理 (NLP) 是一种自然语言处理.
- 健康 公平 研究 健康 公平 研究
背景情况:
- 完整而准确的种族和民族 (RE) 患者信息对于生物医学信息学至关重要.
- 结构化RE数据通常不完整,需要使用临床笔记的NLP方法.
- 现有的NLP模型可能会延续偏见,将负面语言与特定的种族/族群联系在一起.
研究的目的:
- 开发和展示用于临床文本中RE识别的NLP模型审计的方法.
- 测量模型识别特征与人类注释的RE跨度之间的一致性.
- 识别和解决 RE 识别模型中的潜在偏见和危害.
主要方法:
- 开发了用于重新识别NLP模型的审计方法.
- 测量模型衍生突出的特征与手动识别的RE相关文本跨度相比.
- 在临床笔记上训练的NLP模型中评估学到的关联.
主要成果:
- NLP模型在识别RE信息方面展示了表面水平的性能.
- 审计揭示了模型中的学术协会.
- 如果不解决偏差,则确定了来自RE识别模型的未来危害的潜力.
结论:
- 虽然NLP模型对RE数据提取充满希望,但它们的学术关联需要仔细审计.
- 在RE识别模型中未解决的偏见对患者护理和健康公平构成风险.
- 需要进一步的研究和开发,以减轻临床NLP应用中的偏见.
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