发表机构
Virginia Tech; University of Texas at Arlington; University of Melbourne; University of Toronto; University of Virginia; Bangladesh University of Engineering and Technology（弗吉尼亚理工大学; 德克萨斯大学阿灵顿分校; 墨尔本大学; 多伦多大学; 弗吉尼亚大学; 孟加拉国工程技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对MIMIC-IV死亡率预测，提出多指标与交叉性评估框架，揭示公平性干预在不同指标和亚组分辨率下评估差异显著，强调需结合干预目标与估计可靠性进行综合评估。
AI 中文摘要
临床预测中的公平性结论可能强烈依赖于所报告的指标以及评估性能时的人口统计学分辨率。我们重新审视了MIMIC-IV上ICU死亡率预测的这些评估选择，比较了多种公平性干预措施下的预测效用指标和亚组误差指标。作为补充案例研究，我们引入了一种轻量级自适应策略，该策略在不以死亡结局为条件的情况下联合平衡种族-性别-保险的代表性，从而允许将人口统计学代表性平衡与基于结局条件或直接错误率的干预措施分开考察。我们在边缘水平及相应的三向交叉亚组水平上评估其行为，同时考虑更细粒度估计的统计支持。结果表明，干预措施在准确性/AUROC、敏感性和假阳性率方面可能获得截然不同的评估，且边缘人口统计学汇总可能掩盖其组成交叉点内（包括较大亚组中）的异质性误差分布。这些发现强调了在互补指标和亚组分辨率两个层面评估公平性干预措施的重要性，同时需考虑干预目标及亚组估计的可靠性。
英文摘要
Fairness conclusions in clinical prediction can depend strongly on both the metrics reported and the demographic resolution at which performance is evaluated. We revisit these evaluation choices for ICU mortality prediction on MIMIC-IV, comparing predictive-utility and subgroup-error metrics across several fairness interventions. As a complementary case study, we introduce a lightweight adaptation strategy that jointly balances ethnicity--gender--insurance representation without conditioning on mortality outcomes, allowing demographic representation balancing to be examined separately from outcome-conditioned or direct error-rate interventions. We evaluate its behavior at both marginal and corresponding three-way intersectional subgroup levels, while accounting for the statistical support of finer-grained estimates. The results show that interventions can receive substantially different assessments across accuracy/AUROC, sensitivity, and false-positive rate, and that marginal demographic summaries can conceal heterogeneous error profiles within their constituent intersections, including among larger subgroups. These findings highlight the importance of evaluating fairness interventions at both complementary metric and subgroup resolutions, while accounting for the intervention target and the reliability of subgroup estimates.
CommentsNEurlPS TAE workshop 2026 accepted
