  Class imbalance can often degrade predictive performance of supervised
learning algorithms. Balanced classes can be obtained by oversampling exact
copies, with noise, or interpolation between nearest neighbours (as in
traditional SMOTE methods). Oversampling tabular data using augmentation, as is
typical in computer vision tasks, can be achieved with deep generative models.
Deep generative models are effective data synthesisers due to their ability to
capture complex underlying distributions. Synthetic data in healthcare can
enhance interoperability between healthcare providers by ensuring patient
privacy. Equipped with large synthetic datasets which do well to represent
small patient groups, machine learning in healthcare can address the current
challenges of bias and generalisability. This paper evaluates synthetic data
generators ability to synthesise patient electronic health records. We test the
utility of synthetic data for patient outcome classification, observing
increased predictive performance when augmenting imbalanced datasets with
synthetic data.
