mlsanityData to 7 Oct 2026

A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Models

arXiv 2208.01230 ↗First posted 2 Aug 2022cs.LG cs.AI cs.CYcited by 43

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Abstract (arXiv)

Synthetic health data have the potential to mitigate privacy concerns when sharing data to support biomedical research and the development of innovative healthcare applications. Modern approaches for data generation based on machine learning, generative adversarial networks (GAN) methods in particular, continue to evolve and demonstrate remarkable potential. Yet there is a lack of a systematic assessment framework to benchmark methods as they emerge and determine which methods are most appropriate for which use cases. In this work, we introduce a generalizable benchmarking framework to appraise key characteristics of synthetic health data with respect to utility and privacy metrics. We apply the framework to evaluate synthetic data generation methods for electronic health records (EHRs) data from two large academic medical centers with respect to several use cases. The results illustrate that there is a utility-privacy tradeoff for sharing synthetic EHR data. The results further indicate that no method is unequivocally the best on all criteria in each use case, which makes it evident why synthetic data generation methods need to be assessed in context.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). Builds on 15 of its 78 references that are in the map. First posted on arXiv 2 Aug 2022.

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15 of 78 references.

References · 15

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  3. 2020Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease predictioncited by 368
  4. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 329
  5. 2018Differentially Private Generative Adversarial Networkcited by 235
  6. 2017Generating Multi-label Discrete Patient Records using Generative Adversarial Networkscited by 197
  7. 2018Synthetic Data Augmentation using GAN for Improved Liver Lesion Classificationcited by 136
  8. 2021CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detectioncited by 64
  9. 2021Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applicationscited by 47
  10. 2017Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Recordscited by 36
  11. 2020Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluationcited by 36
  12. 2020Generating Electronic Health Records with Multiple Data Types and Constraintscited by 21
3 more
  1. 2020Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Modelscited by 21
  2. 2018Generating Multi-Categorical Samples with Generative Adversarial Networkscited by 17
  3. 2016Going Digital: A Survey on Digitalization and Large Scale Data Analytics in Healthcarecited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 11; 2024: 7; 2025: 15; 2026: 8.

43 papers.

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  1. 2023Machine Learning for Synthetic Data Generation: A Reviewcited by 118
  2. 2023Differentially Private Diffusion Models Generate Useful Synthetic Imagescited by 85
  3. 2025QoQ-Med: Building Multimodal Clinical Foundation Models with Domain-Aware GRPO Trainingcited by 38
  4. 2024SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Datacited by 27
  5. 2023TSGBench: Time Series Generation Benchmarkcited by 24
  6. 2023Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Modelscited by 24
  7. 2024CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelinescited by 22
  8. 2023EHRDiff: Exploring Realistic EHR Synthesis with Diffusion Modelscited by 18
  9. 2023Synthesizing Mixed-type Electronic Health Records using Diffusion Modelscited by 17
  10. 2024Can I trust my fake data -- A comprehensive quality assessment framework for synthetic tabular data in healthcarecited by 17
  11. 2022Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIVcited by 12
  12. 2022Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Surveycited by 9
31 more
  1. 2023Synthesize High-dimensional Longitudinal Electronic Health Records via Hierarchical Autoregressive Language Modelcited by 8
  2. 2023Synthetic Data in Healthcarecited by 8
  3. 2023Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Datacited by 8
  4. 2025A Consensus Privacy Metrics Framework for Synthetic Datacited by 8
  5. 2025Synthetic Data Privacy Metricscited by 7
  6. 2024Guided Discrete Diffusion for Electronic Health Record Generationcited by 5
  7. 2025CEHR-XGPT: A Scalable Multi-Task Foundation Model for Electronic Health Recordscited by 4
  8. 2025Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inferencecited by 4
  9. 2023Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklistcited by 3
  10. 2024Debiasing Synthetic Data Generated by Deep Generative Modelscited by 2
  11. 2023Methods for generating and evaluating synthetic longitudinal patient data: a systematic reviewcited by 1
  12. 2025Zero-shot generation of synthetic neurosurgical data with large language modelscited by 1
  13. 2025PROVCREATOR: Synthesizing Complex Heterogenous Graphs with Node and Edge Attributescited by 1
  14. 2025CancerGUIDE: Cancer Guideline Understanding via Internal Disagreement Estimationcited by 1
  15. 2025SurvDiff: A Diffusion Model for Generating Synthetic Data in Survival Analysiscited by 1
  16. 2024Permissioned Blockchain-based Framework for Ranking Synthetic Data Generatorscited by 0
  17. 2024SSSD-ECG-nle: New Label Embeddings with Structured State-Space Models for ECG generationcited by 0
  18. 2025Generating Multi-Table Time Series EHR from Latent Space with Minimal Preprocessingcited by 0
  19. 2025TCDiff: Triplex Cascaded Diffusion for High-fidelity Multimodal EHRs Generation with Incomplete Clinical Datacited by 0
  20. 2025Synthetic medical data generation: state of the art and application to trauma mechanism classificationcited by 0
  21. 2025Synthetic Survival Data Generation for Heart Failure Prognosis Using Deep Generative Modelscited by 0
  22. 2025Expert Validation of Synthetic Cervical Spine Radiographs Generated with a Denoising Diffusion Probabilistic Modelcited by 0
  23. 2025A Reinforcement Learning Approach to Synthetic Data Generationcited by 0
  24. 2026Evaluation metrics for temporal preservation in synthetic longitudinal patient datacited by 0
  25. 2026Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directionscited by 0
  26. 2026ESL-Bench: An Event-Driven Synthetic Longitudinal Benchmark for Health Agentscited by 0
  27. 2026Evaluating quality in synthetic data generation for large tabular health datasetscited by 0
  28. 2026Generating synthetic electronic health record data using agent-based models to evaluate machine learning robustness under mass casualty incidentscited by 0
  29. 2026Property-Driven Synthetic Data Engineering for Data-Scarce Software Systems: Reflections from the Breast Cancer Domaincited by 0
  30. 2026Improving the Realism of Synthetic Clinical Benchmarks Under Utility Constraintscited by 0
  31. 2026Creation begins with understanding: LLMs as strategy designers for privacy-preserving tabular data synthesiscited by 0