@article{karamiTimEHRImageBasedTime2025,
	title = {{TimEHR}: Image-Based Time Series Generation for Electronic Health Records},
	volume = {29},
	issn = {2168-2208},
	url = {https://ieeexplore.ieee.org/document/11027528},
	doi = {10.1109/JBHI.2025.3577328},
	shorttitle = {{TimEHR}},
	abstract = {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 by using 2D convolutional kernels 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.},
	pages = {9170--9180},
	number = {12},
	journaltitle = {{IEEE} Journal of Biomedical and Health Informatics},
	author = {Karami, Hojjat and Hartley, Mary-Anne and Atienza, David and Ionescu, Anisoara},
	urldate = {2026-06-19},
	date = {2025-12},
	keywords = {Convolutional neural nets, Convolutional neural networks, Data privacy, Electronic health records ({EHRs}), Electronic medical records, Generative adversarial networks, Irregularly sampled time series, Mathematical models, Metadata, Predictive models, Time series analysis, Time series generation, scientific-publication},
}