  Artificial Intelligence in healthcare is a new and exciting frontier and the
possibilities are endless. With deep learning approaches beating human
performances in many areas, the logical next step is to attempt their
application in the health space. For these and other Machine Learning
approaches to produce good results and have their potential realized, the need
for, and importance of, large amounts of accurate data is second to none. This
is a challenge faced by many industries and more so in the healthcare space. We
present an approach of using Variational Autoencoders (VAE's) as an approach to
generating more data for training deeper networks, as well as uncovering
underlying patterns in diagnoses and the patients suffering from them. By
training a VAE, on available data, it was able to learn the latent distribution
of the patient features given the diagnosis. It is then possible, after
training, to sample from the learnt latent distribution to generate new
accurate patient records given the patient diagnosis.
