Health disparities, or inequalities between different patient demographics, are becoming crucial in medical decision-making, especially in Electronic Health Record (EHR) predictive modeling. To ensure the fairness of sensitive attributes, conventional studies mainly adopt calibration or re-weighting methods to balance the performance on among different demographic groups. However, we argue that these methods have some limitations. First, these methods usually mean a trade-off between the model's performance and fairness. Second, many methods completely attribute unfairness to the data collection process, which lacks substantial evidence. In this paper, we provide an empirical study to discover the possibility of using deconfounder to address the disparity issue in healthcare. Our study can be summarized in two parts. The first part is a pilot study demonstrating the exacerbation of disparity when unobserved confounders exist. The second part proposed a novel framework, Parity Medical Deconfounder (PriMeD), to deal with the disparity issue in healthcare datasets. Inspired by the deconfounder theory, PriMeD adopts a Conditional Variational Autoencoder (CVAE) to learn latent factors (substitute confounders) for observational data, and extensive experiments are provided to show its effectiveness.
翻译:为了确保敏感属性的公平性,常规研究主要采用校准或重新加权方法来平衡不同人口群体之间的业绩。然而,我们认为,这些方法有一些局限性。首先,这些方法通常意味着在模型的性能和公平性之间取舍。第二,许多方法完全将不公平归咎于数据收集过程,这缺乏实质性证据。在本文中,我们提供一项实验性研究,以发现使用脱落器解决保健差异问题的可能性。我们的研究可归纳为两部分。第一部分是实验性研究,表明在存在未观察到的相融合者时差异加剧的情况。第二部分提出一个新的框架,即Paity医疗分解器(PrimeD),以处理保健数据集的差异问题。在解析理论的启发下,PriMeD采用了一种解析自动自动解析器(CVAEE),以学习潜在因素(后成型解析器),为观察提供了广泛的数据和实验。