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）领域，因EHR复杂的潜在结构及潜在的选择偏倚而至关重要。实际应用中，在保持模型整体准确性的同时缓解健康差异具有必要性。然而，传统方法常面临准确性与公平性之间的权衡困境，因其无法捕获观测数据之外的潜在因素。为攻克这一挑战，我们提出名为Fair纵向医学去混杂因子（FLMD）的新型模型，旨在实现纵向电子健康记录建模中的公平性与准确性双重目标。受去混杂理论启发，FLMD采用两阶段训练流程：第一阶段，FLMD针对每次就诊捕获未观测的混杂因子，有效表征观测EHR数据以外的潜在医学因素（如患者基因型与生活习惯），这些未观测混杂因子对解决准确性/公平性困境至关重要；第二阶段，FLMD将学习到的潜在表征与其他相关特征相结合进行预测。通过纳入反事实公平性等合理公平性准则，FLMD在保持高预测准确性的同时，最大限度地减少健康差异。我们基于两个真实世界EHR数据集开展了全面实验，验证FLMD的有效性。除在公平性与准确性维度进行基线方法与FLMD变体的对比分析外，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表现，以彰显FLMD在不同场景下的优越性，并深入揭示其能力特性。