mlsanityData to 7 Oct 2026

SynEHRgy: Synthesizing Mixed-Type Structured Electronic Health Records using Decoder-Only Transformers

arXiv 2411.13428 ↗First posted 20 Nov 2024cs.LG cs.AIcited by 5

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Abstract (arXiv)

Generating synthetic Electronic Health Records (EHRs) offers significant potential for data augmentation, privacy-preserving data sharing, and improving machine learning model training. We propose a novel tokenization strategy tailored for structured EHR data, which encompasses diverse data types such as covariates, ICD codes, and irregularly sampled time series. Using a GPT-like decoder-only transformer model, we demonstrate the generation of high-quality synthetic EHRs. Our approach is evaluated using the MIMIC-III dataset, and we benchmark the fidelity, utility, and privacy of the generated data against state-of-the-art models.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 31 of its 47 references that are in the map. First posted on arXiv 20 Nov 2024.

Builds on

31 of 47 references.

References · 31

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19 more
  1. 2024Timer: Generative Pre-trained Transformers Are Large Time Series Modelscited by 198
  2. 2017Generating Multi-label Discrete Patient Records using Generative Adversarial Networkscited by 197
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  6. 2021Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applicationscited by 47
  7. 2020CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare Recordscited by 37
  8. 2020EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoderscited by 28
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  12. 2024CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelinescited by 22
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  14. 2023Synthesizing Mixed-type Electronic Health Records using Diffusion Modelscited by 17
  15. 2022Multi-Label Clinical Time-Series Generation via Conditional GANcited by 16
  16. 2024TimeLDM: Latent Diffusion Model for Unconditional Time Series Generationcited by 10
  17. 2024TimEHR: Image-based Time Series Generation for Electronic Health Recordscited by 8
  18. 2024Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Modelscited by 7
  19. 2023TS-Diffusion: Generating Highly Complex Time Series with Diffusion Modelscited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Datacited by 13
  2. 2026SynthAgent: A Multi-Agent LLM Framework for Realistic Patient Simulation -- A Case Study in Obesity with Mental Health Comorbiditiescited by 2
  3. 2025Critical Challenges and Guidelines in Evaluating Synthetic Tabular Data: A Systematic Reviewcited by 0
  4. 2025Privacy-Preserving Tabular Synthetic Data Generation Using TabularARGNcited by 0
  5. 2026AdaPCLA: Adaptive Prior-Calibrated Logit Adjustment for Long-Tailed Longitudinal EHR Generationcited by 0