TimEHR: Image-based Time Series Generation for Electronic Health Records

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 …

adversarial challenges cs.lg data dimensionality electronic electronic health records gan generate generative generative adversarial network generative models health image images missing values network novel paper records sampling series time series values