In the field of healthcare, electronic health records (EHR) serve as crucial training data for developing machine learning models for diagnosis, treatment, and the management of healthcare resources. However, medical datasets are often imbalanced in terms of sensitive attributes such as race/ethnicity, gender, and age. Machine learning models trained on class-imbalanced EHR datasets perform significantly worse in deployment for individuals of the minority classes compared to those from majority classes, which may lead to inequitable healthcare outcomes for minority groups. To address this challenge, we propose Minority Class Rebalancing through Augmentation by Generative modeling (MCRAGE), a novel approach to augment imbalanced datasets using samples generated by a deep generative model. The MCRAGE process involves training a Conditional Denoising Diffusion Probabilistic Model (CDDPM) capable of generating high-quality synthetic EHR samples from underrepresented classes. We use this synthetic data to augment the existing imbalanced dataset, resulting in a more balanced distribution across all classes, which can be used to train less biased downstream models. We measure the performance of MCRAGE versus alternative approaches using Accuracy, F1 score and AUROC of these downstream models. We provide theoretical justification for our method in terms of recent convergence results for DDPMs.

翻译：在医疗领域，电子健康记录（EHR）是开发用于诊断、治疗和医疗资源管理的机器学习模型的关键训练数据。然而，医疗数据集在种族/民族、性别和年龄等敏感属性上往往存在不平衡。在不平衡的电子健康记录数据集上训练的机器学习模型，在部署时对少数类群体的表现显著低于多数类群体，可能导致少数群体的医疗结果不公平。为应对这一挑战，我们提出基于生成建模的少数类重平衡增强方法（MCRAGE），这是一种利用深度生成模型生成的样本来增强不平衡数据集的新方法。MCRAGE过程包括训练一个条件去噪扩散概率模型（CDDPM），该模型能够生成高质量的代表性不足类别的合成电子健康记录样本。我们使用这些合成数据扩展现有的不平衡数据集，从而在所有类别间实现更平衡的分布，并用于训练偏差较小的下游模型。我们通过下游模型的准确率、F1分数和AUROC指标衡量MCRAGE与替代方法的性能差异，并从去噪扩散概率模型最近的收敛性结果出发，为我们的方法提供理论依据。