Work

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

Year: 2025

Type: article

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.

Source: IEEE Journal of Biomedical and Health Informatics

DOI: 10.1109/jbhi.2025.3577328

Authors: Hojjat Karami, Mary-Anne Hartley, David Atienza, Anisoara Ionescu

Institutions: École Polytechnique Fédérale de Lausanne, Yale University

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,645