  Electronic health records (EHR) often contain different rates of
representation of certain subpopulations (SP). Factors like patient
demographics, clinical condition prevalence, and medical center type contribute
to this underrepresentation. Consequently, when training machine learning
models on such datasets, the models struggle to generalize well and perform
poorly on underrepresented SPs. To address this issue, we propose a novel
ensemble framework that utilizes generative models. Specifically, we train a
GAN-based synthetic data generator for each SP and incorporate synthetic
samples into each SP training set. Ultimately, we train SP-specific prediction
models. To properly evaluate this method, we design an evaluation pipeline with
2 real-world use case datasets, queried from the MIMIC database. Our approach
shows increased model performance over underrepresented SPs. Our code and
models are given as supplementary and will be made available on a public
repository.
