The fairness issue of clinical data modeling, especially on Electronic Health Records (EHRs), is of utmost importance due to EHR's complex latent structure and potential selection bias. It is frequently necessary to mitigate health disparity while keeping the model's overall accuracy in practice. However, traditional methods often encounter the trade-off between accuracy and fairness, as they fail to capture the underlying factors beyond observed data. To tackle this challenge, we propose a novel model called Fair Longitudinal Medical Deconfounder (FLMD) that aims to achieve both fairness and accuracy in longitudinal Electronic Health Records (EHR) modeling. Drawing inspiration from the deconfounder theory, FLMD employs a two-stage training process. In the first stage, FLMD captures unobserved confounders for each encounter, which effectively represents underlying medical factors beyond observed EHR, such as patient genotypes and lifestyle habits. This unobserved confounder is crucial for addressing the accuracy/fairness dilemma. In the second stage, FLMD combines the learned latent representation with other relevant features to make predictions. By incorporating appropriate fairness criteria, such as counterfactual fairness, FLMD ensures that it maintains high prediction accuracy while simultaneously minimizing health disparities. We conducted comprehensive experiments on two real-world EHR datasets to demonstrate the effectiveness of FLMD. Apart from the comparison of baseline methods and FLMD variants in terms of fairness and accuracy, we assessed the performance of all models on disturbed/imbalanced and synthetic datasets to showcase the superiority of FLMD across different settings and provide valuable insights into its capabilities.

翻译：临床数据建模的公平性问题，尤其是电子健康记录（EHR）数据，因其复杂的潜在结构和潜在的选择偏差而至关重要。在实践中，通常需要在保持模型整体准确性的同时减轻健康差异。然而，传统方法常常面临准确性与公平性之间的权衡，因为它们无法捕捉观测数据之外的潜在因素。为应对这一挑战，我们提出了一种新颖模型——公平纵向医疗去混杂器（FLMD），旨在实现纵向电子健康记录（EHR）建模中的公平性与准确性。受去混杂理论启发，FLMD采用两阶段训练过程。第一阶段，FLMD捕获每次就诊中未被观测的混杂因素，这些因素有效代表了观测EHR之外的潜在医疗因素，如患者基因型和生活习惯。这种未被观测的混杂因素对于解决准确性/公平性困境至关重要。第二阶段，FLMD将学习到的潜在表示与其他相关特征相结合进行预测。通过引入适当的公平性准则（如反事实公平性），FLMD确保在保持高预测准确性的同时最小化健康差异。我们在两个真实世界EHR数据集上进行了全面实验，以证明FLMD的有效性。除了在公平性和准确性方面与基线方法和FLMD变体进行对比外，我们还评估了所有模型在扰动/不平衡数据集及合成数据集上的性能，展示了FLMD在不同设置下的优越性，并提供了其能力的深入见解。