  In this paper, we propose a distributed Generative Adversarial Networks
(discGANs) to generate synthetic tabular data specific to the healthcare
domain. While using GANs to generate images has been well studied, little to no
attention has been given to generation of tabular data. Modeling distributions
of discrete and continuous tabular data is a non-trivial task with high
utility. We applied discGAN to model non-Gaussian multi-modal healthcare data.
We generated 249,000 synthetic records from original 2,027 eICU dataset. We
evaluated the performance of the model using machine learning efficacy, the
Kolmogorov-Smirnov (KS) test for continuous variables and chi-squared test for
discrete variables. Our results show that discGAN was able to generate data
with distributions similar to the real data.
