TimEHR: Image-based Time Series Generation for Electronic Health Records
Hojjat Karami Mary-Anne Hartley David Atienza Anisoara Ionescu
Time series in Electronic Health Records (EHRs) present unique challenges for
generative models, such as irregular sampling, missing values, and high
dimensionality. In this paper, we propose a novel generative adversarial
network (GAN) model, TimEHR, to generate time series data from EHRs. In
particular, TimEHR treats time series as images and is based on two conditional
GANs. The first GAN generates missingness patterns, and the second GAN
generates time series values based on the missingness pattern. Experimental
results on three real-world EHR datasets show that TimEHR outperforms
state-of-the-art methods in terms of fidelity, utility, and privacy metrics.