Distributed Conditional GAN (discGAN) For Synthetic Healthcare Data Generation
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Abstract (arXiv)
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.
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References · 7
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2019Modeling Tabular data using Conditional GANcited by 695
- 2017VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learningcited by 199
- 2017Generating Multi-label Discrete Patient Records using Generative Adversarial Networkscited by 197
- 2018Data Synthesis based on Generative Adversarial Networkscited by 184
- 2018MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasetscited by 49
- 2020Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Datacited by 38
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