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在小儿尿路感染模型中测量和减少种族偏见
Joshua W Anderson1, Nader Shaikh2, Shyam Visweswaran3
1Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA.
概括
重新规范UTICalc等临床模型以减少种族偏见可能会提高公平性,但可能会降低预测性能,突出了医疗保健AI中的权衡.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 健康 公平 卫生 公平
背景情况:
- 纳入种族的临床预测模型可能会加剧医疗保健差异.
- 重新规范或优化可以减轻这些模型中的种族偏见.
- 该UTICalc模型旨在减少儿童的尿路导管治疗.
研究的目的:
- 调查尊重UTICalc模型以减少种族偏见的影响.
- 为了比较原始和受尊重的UTICalc版本之间的公平性和预测性能.
- 开发新的模式,以加强种族公平.
主要方法:
- 从UTICalc逻辑回归模型中删除了种族,并引入了两个新功能.
- 使用公平性和预测性绩效指标比较原始和受尊重的UTICalc模型.
- 来自三个新的模型,专门用于提高种族公平性.
主要成果:
- 模型的尊重显示了提高种族公平性的潜力.
- 在所有公平度指标上并未实现同时改善,与不可能的结果保持一致.
- 提高种族公平性有时会导致整体预测性能的下降.
结论:
- 重新规范临床预测模型可以影响公平性和性能.
- 在所有指标上实现完美的公平是具有挑战性的.
- 在修改健康公平模式时,需要仔细考虑权衡.
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