mlsanityData to 7 Oct 2026

Synthesizing Mixed-type Electronic Health Records using Diffusion Models

arXiv 2302.14679 ↗First posted 28 Feb 2023cs.LG cs.CLcited by 17

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Electronic Health Records (EHRs) contain sensitive patient information, which presents privacy concerns when sharing such data. Synthetic data generation is a promising solution to mitigate these risks, often relying on deep generative models such as Generative Adversarial Networks (GANs). However, recent studies have shown that diffusion models offer several advantages over GANs, such as generation of more realistic synthetic data and stable training in generating data modalities, including image, text, and sound. In this work, we investigate the potential of diffusion models for generating realistic mixed-type tabular EHRs, comparing TabDDPM model with existing methods on four datasets in terms of data quality, utility, privacy, and augmentation. Our experiments demonstrate that TabDDPM outperforms the state-of-the-art models across all evaluation metrics, except for privacy, which confirms the trade-off between privacy and utility.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 21 of its 31 references that are in the map. First posted on arXiv 28 Feb 2023.

Builds on

21 of 31 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model8 Feb
  2. 2021Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions10 Feb

Other references · 19

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  3. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  4. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  5. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  6. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,665
  7. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,618
  8. 2020DiffWave: A Versatile Diffusion Model for Audio Synthesiscited by 1,339
  9. 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,155
  10. 2022TabDDPM: Modelling Tabular Data with Diffusion Modelscited by 373
  11. 2019BEHRT: Transformer for Electronic Health Recordscited by 258
  12. 2017Generating Multi-label Discrete Patient Records using Generative Adversarial Networkscited by 197
7 more
  1. 2021CTAB-GAN: Effective Table Data Synthesizingcited by 156
  2. 2021Symbolic Music Generation with Diffusion Modelscited by 152
  3. 2021Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applicationscited by 47
  4. 2022A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Modelscited by 43
  5. 2020CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare Recordscited by 37
  6. 2022A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sourcescited by 13
  7. 2013Routes for breaching and protecting genetic privacycited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 6; 2025: 4; 2026: 1.

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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. 2023Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Timecited by 20
  5. 2023EHRDiff: Exploring Realistic EHR Synthesis with Diffusion Modelscited by 18
  6. 2023The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasetscited by 14
  7. 2025The DCR Delusion: Measuring the Privacy Risk of Synthetic Datacited by 11
  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. 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. 2023Privacy Measurement in Tabular Synthetic Data: State of the Art and Future Research Directionscited by 4
5 more
  1. 2025Contrastive Learning-Based privacy metrics in Tabular Synthetic Datasetscited by 2
  2. 2024Fairness-Optimized Synthetic EHR Generation for Arbitrary Downstream Predictive Taskscited by 1
  3. 2024Mpox Detection Advanced: Rapid Epidemic Response Through Synthetic Datacited by 1
  4. 2024SynthVision -- Harnessing Minimal Input for Maximal Output in Computer Vision Models using Synthetic Image datacited by 0
  5. 2026CDMT-EHR: A Continuous-Time Diffusion Framework for Generating Mixed-Type Time-Series Electronic Health Recordscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models
  2. 2023Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models
  3. 2025An Integrated Approach to AI-Generated Content in e-health
  4. 2020Tabular GANs for uneven distribution
  5. 2024Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges
  6. 2024MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data
  7. 2023Generating tabular datasets under differential privacy
  8. 2024DP-TLDM: Differentially Private Tabular Latent Diffusion Model