Guidelines for the management of atherosclerotic cardiovascular disease (ASCVD) recommend the use of risk stratification models to identify patients most likely to benefit from cholesterol-lowering and other therapies. These models have differential performance across race and gender groups with inconsistent behavior across studies, potentially resulting in an inequitable distribution of beneficial therapy. In this work, we leverage adversarial learning and a large observational cohort extracted from electronic health records (EHRs) to develop a "fair" ASCVD risk prediction model with reduced variability in error rates across groups. We empirically demonstrate that our approach is capable of aligning the distribution of risk predictions conditioned on the outcome across several groups simultaneously for models built from high-dimensional EHR data. We also discuss the relevance of these results in the context of the empirical trade-off between fairness and model performance.

利用对抗训练和电子健康记录中提取的大型观察性队列，我们制定了较少跨群体误差率差异性的“公平”的 ASCVD 风险预测模型，并在高维电子健康记录数据的模型上证明了我们的方法可以对多个群体同时进行风险预测分布对齐。

建立动脉粥样硬化心血管疾病风险公正模型