  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.
