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 samples 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, thereby achieving a more balanced distribution across all classes, which can be used to train an unbiased machine learning model. We measure the performance of MCRAGE versus alternative approaches using Accuracy, F1 score and AUROC. We provide theoretical justification for our method in terms of recent convergence results for DDPMs with minimal assumptions.

翻译：在医疗领域，电子健康记录（EHR）是开发用于诊断、治疗及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、年龄等敏感属性上往往呈现不平衡性。基于类别不平衡的EHR数据集训练的机器学习模型，在部署时对少数类别个体的表现显著差于多数类别样本，这可能导致少数群体在医疗结果上遭受不公平对待。为应对这一挑战，我们提出"通过生成式建模增强实现少数类别再平衡"（MCRAGE）方法——一种利用深度生成模型生成的样本来扩充不平衡数据集的新颖方法。MCRAGE流程包括训练一个能够从欠表示类别生成高质量合成EHR样本的条件去噪扩散概率模型（CDDPM）。我们使用这些合成数据扩充现有不平衡数据集，从而在所有类别间实现更平衡的分布，并据此训练无偏的机器学习模型。我们通过准确率、F1分数和AUROC指标衡量MCRAGE与替代方法的性能差异，并基于去噪扩散概率模型（DDPM）在最小假设条件下的近期收敛性结果，为该方法提供理论依据。