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.

本文提出一种分布式生成对抗网络 (discGANs) 用于生成针对医疗保健领域的合成表格数据，特别是离散和连续医疗数据的分布建模。通过使用该方法，我们从原始的 2,027 个电子 ICU 数据集中生成了 249,000 个合成数据记录，并使用 Kolmogorov-Smirnov (KS) 检验和卡方检验评估了其机器学习效率和性能，结果显示 discGAN 能够生成与实际数据分布相似的数据。