mlsanityData to 7 Oct 2026

Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models

arXiv 2303.12281 ↗First posted 22 Mar 2023cs.LG q-bio.QMcited by 9

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

This paper presents a novel approach to simulating electronic health records (EHRs) using diffusion probabilistic models (DPMs). Specifically, we demonstrate the effectiveness of DPMs in synthesising longitudinal EHRs that capture mixed-type variables, including numeric, binary, and categorical variables. To our knowledge, this represents the first use of DPMs for this purpose. We compared our DPM-simulated datasets to previous state-of-the-art results based on generative adversarial networks (GANs) for two clinical applications: acute hypotension and human immunodeficiency virus (ART for HIV). Given the lack of similar previous studies in DPMs, a core component of our work involves exploring the advantages and caveats of employing DPMs across a wide range of aspects. In addition to assessing the realism of the synthetic datasets, we also trained reinforcement learning (RL) agents on the synthetic data to evaluate their utility for supporting the development of downstream machine learning models. Finally, we estimated that our DPM-simulated datasets are secure and posed a low patient exposure risk for public access.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 56 of its 106 references that are in the map. First posted on arXiv 22 Mar 2023.

Builds on

56 of 106 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023EHRDiff: Exploring Realistic EHR Synthesis with Diffusion Models10 Mar

Other references · 55

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38 more
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  16. 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANscited by 515
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50 most cited of 55 All 55 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 3; 2026: 3.

9 papers.

  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. 2025Diffusion and Flow Matching Models for Tabular Data: A Surveycited by 8
  5. 2023A Comprehensive Survey on Generative Diffusion Models for Structured Datacited by 7
  6. 2025Attention-Based Synthetic Data Generation for Calibration-Enhanced Survival Analysis: A Case Study for Chronic Kidney Disease Using Electronic Health Recordscited by 1
  7. 2026From Statistical Fidelity to Clinical Consistency: Scalable Generation and Auditing of Synthetic Patient Trajectoriescited by 1
  8. 2026PRIME-CVD: A Parametrically Rendered Informatics Medical Environment for Education in Cardiovascular Risk Modellingcited by 1
  9. 2026Synthetic but Not Realistic: The Evaluation Challenge in Generative Modelling for Structured Electronic Medical Recordscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Synthesizing Mixed-type Electronic Health Records using Diffusion Models
  2. 2023MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model
  3. 2024Guided Discrete Diffusion for Electronic Health Record Generation
  4. 2021Synthetic Acute Hypotension and Sepsis Datasets Based on MIMIC-III and Published as Part of the Health Gym Project
  5. 2024Addressing Small and Imbalanced Medical Image Datasets Using Generative Models: A Comparative Study of DDPM and PGGANs with Random and Greedy K Sampling
  6. 2024Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges
  7. 2024Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models
  8. 2026CDMT-EHR: A Continuous-Time Diffusion Framework for Generating Mixed-Type Time-Series Electronic Health Records