  Electronic health records (EHR) contain a wealth of biomedical information,
serving as valuable resources for the development of precision medicine
systems. However, privacy concerns have resulted in limited access to
high-quality and large-scale EHR data for researchers, impeding progress in
methodological development. Recent research has delved into synthesizing
realistic EHR data through generative modeling techniques, where a majority of
proposed methods relied on generative adversarial networks (GAN) and their
variants for EHR synthesis. Despite GAN-based methods attaining
state-of-the-art performance in generating EHR data, these approaches are
difficult to train and prone to mode collapse. Recently introduced in
generative modeling, diffusion models have established cutting-edge performance
in image generation, but their efficacy in EHR data synthesis remains largely
unexplored. In this study, we investigate the potential of diffusion models for
EHR data synthesis and introduce a novel method, EHRDiff. Through extensive
experiments, EHRDiff establishes new state-of-the-art quality for synthetic EHR
data, protecting private information in the meanwhile.
