Comparison of Conditional and Non-Conditional Tabular GANs in FID Score for High-Cardinality Datasets

Abstract

Abstract: This paper presents a novel approach to simulating electronic health records (EHRs) using diffusion probabilistic models (DPMs). Specifically, we demonstrate the effectiveness of DPMs in synthesising longitudinal EHRs that capture mixed-type variables, including numeric, binary, and categorical variables. To our knowledge, this represents the first use of DPMs for this purpose. We compared our DPM-simulated datasets to previous state-of-the-art results based on generative adversarial networks (GANs) for two clinical applications: acute hypotension and human immunodeficiency virus (ART for HIV)

Research Question

How does the conditional tabular GAN (ciDATGAN) compare to non-conditional tabular GANs in terms of FID score when generating large-scale datasets with high-cardinality features?

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