Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIV. (arXiv:2208.08655v1 [cs.LG])

Clinical data usually cannot be freely distributed due to their highly
confidential nature and this hampers the development of machine learning in the
healthcare domain. One way to mitigate this problem is by generating realistic
synthetic datasets using generative adversarial networks (GANs). However, GANs
are known to suffer from mode collapse and thus creating outputs of low
diveristy. In this paper, we extend the classic GAN setup with an external
memory to replay features from real samples. Using antiretroviral therapy …

arxiv data example generative adversarial networks lg networks