在小儿尿路感染模型中测量并减少种族偏见。

Joshua W Anderson, Nader Shaikh, Shyam Visweswaran
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引用次数: 0

摘要

将种族作为预测因素的临床预测模型有可能加剧医疗保健中的差异。可以对此类模型进行重新设计,排除种族因素,或对其进行优化,以减少种族偏见。我们在一个预测模型--UTICalc--中研究了这种重新设计的影响,该模型旨在减少疑似尿路感染的幼儿导管插入术。为了减少种族偏差,UTICalc 逻辑回归模型中删除了种族,代之以两个新特征。我们使用公平性和预测性能指标对两个版本的UTICalc进行了比较,以了解对种族偏见的影响。此外,我们还为UTICalc 建立了三个新模型,以专门改善种族公平性。我们的结果表明,正如之前描述的不可能性结果所预测的那样,公平性不可能在所有公平性指标上同时得到改善,模型的重新设计可能会改善种族公平性,但会降低整体预测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measuring and Reducing Racial Bias in a Pediatric Urinary Tract Infection Model.

Clinical predictive models that include race as a predictor have the potential to exacerbate disparities in healthcare. Such models can be respecified to exclude race or optimized to reduce racial bias. We investigated the impact of such respecifications in a predictive model - UTICalc - which was designed to reduce catheterizations in young children with suspected urinary tract infections. To reduce racial bias, race was removed from the UTICalc logistic regression model and replaced with two new features. We compared the two versions of UTICalc using fairness and predictive performance metrics to understand the effects on racial bias. In addition, we derived three new models for UTICalc to specifically improve racial fairness. Our results show that, as predicted by previously described impossibility results, fairness cannot be simultaneously improved on all fairness metrics, and model respecification may improve racial fairness but decrease overall predictive performance.

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