mlsanityData to 7 Oct 2026

MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model

arXiv 2302.04355 ↗First posted 8 Feb 2023cs.LG cs.AI cs.CRcited by 22

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Due to patient privacy protection concerns, machine learning research in healthcare has been undeniably slower and limited than in other application domains. High-quality, realistic, synthetic electronic health records (EHRs) can be leveraged to accelerate methodological developments for research purposes while mitigating privacy concerns associated with data sharing. The current state-of-the-art model for synthetic EHR generation is generative adversarial networks, which are notoriously difficult to train and can suffer from mode collapse. Denoising Diffusion Probabilistic Models, a class of generative models inspired by statistical thermodynamics, have recently been shown to generate high-quality synthetic samples in certain domains. It is unknown whether these can generalize to generation of large-scale, high-dimensional EHRs. In this paper, we present a novel generative model based on diffusion models that is the first successful application on electronic health records. Our model proposes a mechanism to perform class-conditional sampling to preserve label information. We also introduce a new sampling strategy to accelerate the inference speed. We empirically show that our model outperforms existing state-of-the-art synthetic EHR generation methods.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 20 of its 39 references that are in the map. First posted on arXiv 8 Feb 2023.

Builds on

20 of 39 references.

References · 20

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2020Denoising Diffusion Implicit Modelscited by 9,655
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  6. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  7. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  8. 2022Generative Adversarial Networkscited by 5,603
  9. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  10. 2018Neural Ordinary Differential Equationscited by 3,733
  11. 2021Score-based Generative Modeling in Latent Spacecited by 634
  12. 2020WaveGrad: Estimating Gradients for Waveform Generationcited by 620
8 more
  1. 2018Scalable and accurate deep learning for electronic health recordscited by 275
  2. 2017Generating Multi-label Discrete Patient Records using Generative Adversarial Networkscited by 197
  3. 2013Deep AutoRegressive Networkscited by 60
  4. 2020CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare Recordscited by 37
  5. 2020EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoderscited by 28
  6. 2020Generating Electronic Health Records with Multiple Data Types and Constraintscited by 21
  7. 2022Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populationscited by 14
  8. 2022AUTM Flow: Atomic Unrestricted Time Machine for Monotonic Normalizing Flowscited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 8; 2025: 5; 2026: 3.

22 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. 2023EHRDiff: Exploring Realistic EHR Synthesis with Diffusion Modelscited by 18
  5. 2023Synthesizing Mixed-type Electronic Health Records using Diffusion Modelscited by 17
  6. 2023Towards Generative Modeling of Urban Flow through Knowledge-enhanced Denoising Diffusioncited by 17
  7. 2024Balanced Mixed-Type Tabular Data Synthesis with Diffusion Modelscited by 12
  8. 2025Diffusion and Flow Matching Models for Tabular Data: A Surveycited by 8
  9. 2023A Comprehensive Survey on Generative Diffusion Models for Structured Datacited by 7
  10. 2024Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Modelscited by 7
  11. 2024Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspectivecited by 6
  12. 2024Guided Discrete Diffusion for Electronic Health Record Generationcited by 5
10 more
  1. 2024How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputationcited by 5
  2. 2024SynEHRgy: Synthesizing Mixed-Type Structured Electronic Health Records using Decoder-Only Transformerscited by 5
  3. 2023Reducing operator complexity in Algebraic Multigrid with Machine Learning Approachescited by 2
  4. 2024Fairness-Optimized Synthetic EHR Generation for Arbitrary Downstream Predictive Taskscited by 1
  5. 2025Generating Realistic Multi-Beat ECG Signalscited by 1
  6. 2026What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditionscited by 1
  7. 2025High-Fidelity Synthetic ECG Generation via Mel-Spectrogram Informed Diffusion Trainingcited by 0
  8. 2025Improving Cardiac Risk Prediction Using Data Generation Techniquescited by 0
  9. 2026CDMT-EHR: A Continuous-Time Diffusion Framework for Generating Mixed-Type Time-Series Electronic Health Recordscited by 0
  10. 2026SynEHR: Joint Modeling Inter-visit Temporal Evolution and Intra-visit Clinical Structure for Longitudinal EHR Synthesiscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models
  2. 2025Diffmv: A Unified Diffusion Framework for Healthcare Predictions with Random Missing Views and View Laziness
  3. 2025An Integrated Approach to AI-Generated Content in e-health
  4. 2022Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
  5. 2026Efficient Generative Prediction for EHR Foundation Models: The SCOPE and REACH Estimators
  6. 2023MCRAGE: Synthetic Healthcare Data for Fairness
  7. 2024Unconditional Latent Diffusion Models Memorize Patient Imaging Data: Implications for Openly Sharing Synthetic Data
  8. 2025A Reinforcement Learning Approach to Synthetic Data Generation