@misc{karami_timehr_2024,
	title = {{TimEHR}: Image-based Time Series Generation for Electronic Health Records},
	rights = {Creative Commons Attribution 4.0 International},
	url = {https://arxiv.org/abs/2402.06318},
	doi = {10.48550/ARXIV.2402.06318},
	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 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.},
	publisher = {{arXiv}},
	eprinttype = {arxiv},
	eprint = {https://arxiv.org/abs/2402.06318},
	author = {Karami, Hojjat and Hartley, Mary-Anne and Atienza, David and Ionescu, Anisoara},
	urldate = {2024-11-26},
	date = {2024},
	note = {Version Number: 1},
	keywords = {{FOS}: Computer and information sciences, Machine Learning (cs.{LG})},
}