Synthesising Electronic Health Records: Cystic Fibrosis Patient Group. (arXiv:2201.05400v1 [cs.LG])

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 …

arxiv electronic health patient records