mlsanityData to 7 Oct 2026

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

arXiv 2402.06318 ↗First posted 9 Feb 2024cs.LGcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 34 of its 57 references that are in the map. First posted on arXiv 9 Feb 2024.

Builds on

34 of 57 references.

References · 34

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22 more
  1. 2015Doctor AI: Predicting Clinical Events via Recurrent Neural Networkscited by 205
  2. 2019Set Functions for Time Seriescited by 137
  3. 2021Graph-Guided Network for Irregularly Sampled Multivariate Time Seriescited by 110
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  7. 2024A Closer Look at AUROC and AUPRC under Class Imbalancecited by 56
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  16. 2023Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Modelscited by 24
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  18. 2019Multivariate Time Series Classification using Dilated Convolutional Neural Networkcited by 9
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  20. 2023TS-Diffusion: Generating Highly Complex Time Series with Diffusion Modelscited by 6
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 3.

8 papers.

  1. 2025Harnessing Vision Models for Time Series Analysis: A Surveycited by 26
  2. 2025TarDiff: Target-Oriented Diffusion Guidance for Synthetic Electronic Health Record Time Series Generationcited by 8
  3. 2025From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?cited by 7
  4. 2024SynEHRgy: Synthesizing Mixed-Type Structured Electronic Health Records using Decoder-Only Transformerscited by 5
  5. 2025ViFusionTST: Deep Fusion of Time-Series Image Representations from Load Signals for Early Bed-Exit Predictioncited by 0
  6. 2026CDMT-EHR: A Continuous-Time Diffusion Framework for Generating Mixed-Type Time-Series Electronic Health Recordscited by 0
  7. 2026PRISM: Powerful Time Series to Image (TS2I) Representations for Multivariate Anomaly Detectioncited by 0
  8. 2026ChronoFlow: Hierarchical Flow Matching for Irregular Time Series Generationcited by 0