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.

为了解决电子健康记录（EHR）建模中的公平问题，本文提出了一种新型模型 —— 公平长期医学去卷积（FLMD），旨在在纵向 EHR 建模中实现公平和准确性。通过引入未观测的混淆变量，FLMD 能够兼顾预测准确性和缩小健康差距，同时通过对真实数据集的全面实验验证了其有效性。