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，特别是在电子健康记录（EHR）领域，因其复杂的潜在结构和潜在的选择偏倚而至关重要。在实践中，常在保持模型整体准确性的同时减轻健康差异。然而，传统方法由于未能捕捉观测数据之外的潜在因素，常常面临准确性与公平性之间的权衡。为应对这一挑战，我们提出了一种名为纵向医疗公平去混杂因子（FLMD）的新模型，旨在纵向电子健康记录（EHR）建模中同时实现公平性和准确性。受去混杂因子理论启发，FLMD采用两阶段训练过程。第一阶段，FLMD捕捉每次就诊的未观测混杂因子，有效表示观测EHR之外的潜在医疗因素，如患者基因型和生活习惯。这一未观测混杂因子对于解决准确性/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结合以进行预测。通过融入如反事实公平性等适当公平性标准，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验证FLMD的有效性。除了在公平性和准确性方面与基线方法和FLMD变体进行比较外，我们还评估了所有模型在受干扰/不平衡及合成数据集上的性能，以展示FLMD在不同设置下的优越性，并为其能力提供宝贵见解。