Generative Adversarial Networks for Synthetic Longitudinal Electronic Health Records Enabling Cardiovascular Digital Twins

The silent progression of cardiovascular disease (CVD) is a major problem particularly in CVD prevention. New techniques enabled by the rise of electronic health records may facilitate CVD prevention. Both public health research and big data applications, such as digital twins, are dependent on access to longitudinal and sensitive data; a challenge which may be facilitated by access to longitudinal synthetic data. In this study, we establish a fidelity benchmark for longitudinal synthetic data by extending a well-known method for cross-sectional synthetic data to a longitudinal application within CVD. We find that the univariate distributional difference between the real and the synthetic data is kept low and that pairwise relations are preserved in the synthetic data. Further, we see that the variablewise temporal trends are preserved, yet may be more extensively studied and have some room for improvement. The results of this study is important to enable future studies within public health prevention and cardiovascular digital twins.