Synthesizing Mixed-type Electronic Health Records using Diffusion Models. (arXiv:2302.14679v2 [cs.LG] UPDATED)

Electronic Health Records (EHRs) contain sensitive patient information, which
presents privacy concerns when sharing such data. Synthetic data generation is
a promising solution to mitigate these risks, often relying on deep generative
models such as Generative Adversarial Networks (GANs). However, recent studies
have shown that diffusion models offer several advantages over GANs, such as
generation of more realistic synthetic data and stable training in generating
data modalities, including image, text, and sound. In this work, we investigate
the potential of …

arxiv data deep generative models diffusion diffusion models electronic electronic health records gans generative generative adversarial networks generative models health information mixed networks patient privacy records risks solution studies synthetic synthetic data type