  Synthesizing electronic health records (EHR) data has become a preferred
strategy to address data scarcity, improve data quality, and model fairness in
healthcare. However, existing approaches for EHR data generation predominantly
rely on state-of-the-art generative techniques like generative adversarial
networks, variational autoencoders, and language models. These methods
typically replicate input visits, resulting in inadequate modeling of temporal
dependencies between visits and overlooking the generation of time information,
a crucial element in EHR data. Moreover, their ability to learn visit
representations is limited due to simple linear mapping functions, thus
compromising generation quality. To address these limitations, we propose a
novel EHR data generation model called EHRPD. It is a diffusion-based model
designed to predict the next visit based on the current one while also
incorporating time interval estimation. To enhance generation quality and
diversity, we introduce a novel time-aware visit embedding module and a
pioneering predictive denoising diffusion probabilistic model (PDDPM).
Additionally, we devise a predictive U-Net (PU-Net) to optimize P-DDPM.We
conduct experiments on two public datasets and evaluate EHRPD from fidelity,
privacy, and utility perspectives. The experimental results demonstrate the
efficacy and utility of the proposed EHRPD in addressing the aforementioned
limitations and advancing EHR data generation.
