  Due to patient privacy protection concerns, machine learning research in
healthcare has been undeniably slower and limited than in other application
domains. High-quality, realistic, synthetic electronic health records (EHRs)
can be leveraged to accelerate methodological developments for research
purposes while mitigating privacy concerns associated with data sharing. The
current state-of-the-art model for synthetic EHR generation is generative
adversarial networks, which are notoriously difficult to train and can suffer
from mode collapse. Denoising Diffusion Probabilistic Models, a class of
generative models inspired by statistical thermodynamics, have recently been
shown to generate high-quality synthetic samples in certain domains. It is
unknown whether these can generalize to generation of large-scale,
high-dimensional EHRs. In this paper, we present a novel generative model based
on diffusion models that is the first successful application on electronic
health records. Our model proposes a mechanism to perform class-conditional
sampling to preserve label information. We also introduce a new sampling
strategy to accelerate the inference speed. We empirically show that our model
outperforms existing state-of-the-art synthetic EHR generation methods.
