ArXiv Preprint
Electronic health records (EHR) contain vast biomedical knowledge and are
rich resources for developing precise medicine systems. However, due to privacy
concerns, there are limited high-quality EHR data accessible to researchers
hence hindering the advancement of methodologies. Recent research has explored
using generative modelling methods to synthesize realistic EHR data, and most
proposed methods are based on the generative adversarial network (GAN) and its
variants for EHR synthesis. Although GAN-style methods achieved
state-of-the-art performance in generating high-quality EHR data, such methods
are hard to train and prone to mode collapse. Diffusion models are recently
proposed generative modelling methods and set cutting-edge performance in image
generation. The performance of diffusion models in realistic EHR synthesis is
rarely explored. In this work, we explore whether the superior performance of
diffusion models can translate to the domain of EHR synthesis and propose a
novel EHR synthesis method named EHRDiff. Through comprehensive experiments,
EHRDiff achieves new state-of-the-art performance for the quality of synthetic
EHR data and can better protect private information in real training EHRs in
the meanwhile.
Hongyi Yuan, Songchi Zhou, Sheng Yu
2023-03-10
