mlsanityData to 7 Oct 2026

EHRDiff: Exploring Realistic EHR Synthesis with Diffusion Models

arXiv 2303.05656 ↗First posted 10 Mar 2023cs.LG cs.CVcited by 18

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Electronic health records (EHR) contain a wealth of biomedical information, serving as valuable resources for the development of precision medicine systems. However, privacy concerns have resulted in limited access to high-quality and large-scale EHR data for researchers, impeding progress in methodological development. Recent research has delved into synthesizing realistic EHR data through generative modeling techniques, where a majority of proposed methods relied on generative adversarial networks (GAN) and their variants for EHR synthesis. Despite GAN-based methods attaining state-of-the-art performance in generating EHR data, these approaches are difficult to train and prone to mode collapse. Recently introduced in generative modeling, diffusion models have established cutting-edge performance in image generation, but their efficacy in EHR data synthesis remains largely unexplored. In this study, we investigate the potential of diffusion models for EHR data synthesis and introduce a novel method, EHRDiff. Through extensive experiments, EHRDiff establishes new state-of-the-art quality for synthetic EHR data, protecting private information in the meanwhile.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 29 of its 50 references that are in the map. First posted on arXiv 10 Mar 2023.

Builds on

29 of 50 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Synthesizing Mixed-type Electronic Health Records using Diffusion Models28 Feb
  2. 2023MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model8 Feb

Other references · 27

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Denoising Diffusion Implicit Modelscited by 9,655
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  5. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  6. 2016Layer Normalizationcited by 5,579
  7. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 2,994
  8. 2020DiffWave: A Versatile Diffusion Model for Audio Synthesiscited by 1,339
  9. 2022Diffusion-LM Improves Controllable Text Generationcited by 1,006
  10. 2019ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmissioncited by 487
  11. 2017Generating Multi-label Discrete Patient Records using Generative Adversarial Networkscited by 197
  12. 2016Mode Regularized Generative Adversarial Networkscited by 123
15 more
  1. 2019Fair Generative Modeling via Weak Supervisioncited by 114
  2. 2018ECG Heartbeat Classification: A Deep Transferable Representationcited by 76
  3. 2019Bias Correction of Learned Generative Models using Likelihood-Free Importance Weightingcited by 70
  4. 2022SeqDiffuSeq: Text Diffusion with Encoder-Decoder Transformerscited by 63
  5. 2018Banach Wasserstein GANcited by 56
  6. 2022A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Modelscited by 43
  7. 2020CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare Recordscited by 37
  8. 2021Measuring Fairness in Generative Modelscited by 31
  9. 2020EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoderscited by 28
  10. 2022Fair Generative Models via Transfer Learningcited by 27
  11. 2022PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learningcited by 22
  12. 2020Generating Electronic Health Records with Multiple Data Types and Constraintscited by 21
  13. 2018On Catastrophic Forgetting and Mode Collapse in Generative Adversarial Networkscited by 16
  14. 2020Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluationcited by 14
  15. 2021Efficient Symptom Inquiring and Diagnosis via Adaptive Alignment of Reinforcement Learning and Classificationcited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 4; 2025: 7; 2026: 3.

18 papers.

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  1. 2024A Survey on Diffusion Models for Time Series and Spatio-Temporal Datacited by 66
  2. 2025A Comprehensive Survey of Synthetic Tabular Data Generationcited by 27
  3. 2023Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Modelscited by 24
  4. 2023Towards Generative Modeling of Urban Flow through Knowledge-enhanced Denoising Diffusioncited by 17
  5. 2023Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Modelscited by 9
  6. 2025Vision-Enhanced Time Series Forecasting via Latent Diffusion Modelscited by 9
  7. 2025Diffusion and Flow Matching Models for Tabular Data: A Surveycited by 8
  8. 2023A Comprehensive Survey on Generative Diffusion Models for Structured Datacited by 7
  9. 2024Improve Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspectivecited by 5
  10. 2024Guided Discrete Diffusion for Electronic Health Record Generationcited by 5
  11. 2024SynEHRgy: Synthesizing Mixed-Type Structured Electronic Health Records using Decoder-Only Transformerscited by 5
  12. 2025DFAMS: Dynamic-flow guided Federated Alignment based Multi-prototype Searchcited by 2
6 more
  1. 2026SynthAgent: A Multi-Agent LLM Framework for Realistic Patient Simulation -- A Case Study in Obesity with Mental Health Comorbiditiescited by 2
  2. 2025Generating Realistic Multi-Beat ECG Signalscited by 1
  3. 2025TCDiff: Triplex Cascaded Diffusion for High-fidelity Multimodal EHRs Generation with Incomplete Clinical Datacited by 0
  4. 2025A Reinforcement Learning Approach to Synthetic Data Generationcited by 0
  5. 2026CDMT-EHR: A Continuous-Time Diffusion Framework for Generating Mixed-Type Time-Series Electronic Health Recordscited by 0
  6. 2026Accelerating Reproducible Research in Synthetic EHR Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models
  2. 2025An Integrated Approach to AI-Generated Content in e-health
  3. 2024Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges
  4. 2025Diffmv: A Unified Diffusion Framework for Healthcare Predictions with Random Missing Views and View Laziness
  5. 2024Deep Generative Models for 3D Medical Image Synthesis
  6. 2023Recent Advances in Generative AI for Healthcare Applications
  7. 2024Unconditional Latent Diffusion Models Memorize Patient Imaging Data: Implications for Openly Sharing Synthetic Data
  8. 2023Beware of diffusion models for synthesizing medical images -- A comparison with GANs in terms of memorizing brain MRI and chest x-ray images