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、性别和年龄等敏感属性上往往存在不平衡问题。在类别不平衡的EHR数据集上训练的机器学习模型，其对少数类别个体的部署性能显著低于多数类别样本，可能导致少数群体获得不公平的医疗结果。为解决这一挑战，我们提出了通过生成式建模进行少数类别重平衡增强（MCRAGE）方法——一种利用深度生成模型生成样本以增强不平衡数据集的新颖方法。MCRAGE流程包括训练一个条件去噪扩散概率模型（CDDPM），该模型能够从代表性不足的类别生成高质量的合成EHR样本。我们利用这些合成数据增强现有不平衡数据集，从而实现跨所有类别的更均衡分布，并可用于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，并结合DDPM在最小假设下的最新收敛结果，为该方法提供理论依据。