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Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applications

arXiv 2112.12047 ↗First posted 22 Dec 2021cs.LGcited by 47

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Abstract (arXiv)

The recent availability of electronic health records (EHRs) have provided enormous opportunities to develop artificial intelligence (AI) algorithms. However, patient privacy has become a major concern that limits data sharing across hospital settings and subsequently hinders the advances in AI. Synthetic data, which benefits from the development and proliferation of generative models, has served as a promising substitute for real patient EHR data. However, the current generative models are limited as they only generate single type of clinical data for a synthetic patient, i.e., either continuous-valued or discrete-valued. To mimic the nature of clinical decision-making which encompasses various data types/sources, in this study, we propose a generative adversarial network (GAN) entitled EHR-M-GAN which simultaneously synthesizes mixed-type timeseries EHR data. EHR-M-GAN is capable of capturing the multidimensional, heterogeneous, and correlated temporal dynamics in patient trajectories. We have validated EHR-M-GAN on three publicly-available intensive care unit databases with records from a total of 141,488 unique patients, and performed privacy risk evaluation of the proposed model. EHR-M-GAN has demonstrated its superiority over state-of-the-art benchmarks for synthesizing clinical timeseries with high fidelity, while addressing the limitations regarding data types and dimensionality in the current generative models. Notably, prediction models for outcomes of intensive care performed significantly better when training data was augmented with the addition of EHR-M-GAN-generated timeseries. EHR-M-GAN may have use in developing AI algorithms in resource-limited settings, lowering the barrier for data acquisition while preserving patient privacy.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). Builds on 35 of its 115 references that are in the map. First posted on arXiv 22 Dec 2021.

Builds on

35 of 115 references.

References · 35

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23 more
  1. 2018Pros and Cons of GAN Evaluation Measurescited by 224
  2. 2016Coupled Generative Adversarial Networkscited by 216
  3. 2017Generating Multi-label Discrete Patient Records using Generative Adversarial Networkscited by 197
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 8; 2024: 11; 2025: 14; 2026: 10.

47 papers.

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  1. 2022A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Modelscited by 43
  2. 2023Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Modelscited by 24
  3. 2024Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challengescited by 24
  4. 2024CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelinescited by 22
  5. 2022The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithmscited by 19
  6. 2023Synthesizing Mixed-type Electronic Health Records using Diffusion Modelscited by 17
  7. 2024Can I trust my fake data -- A comprehensive quality assessment framework for synthetic tabular data in healthcarecited by 17
  8. 2022A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sourcescited by 13
  9. 2025TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Datacited by 13
  10. 2022Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIVcited by 12
  11. 2024A Review on Generative AI Models for Synthetic Medical Text, Time Series, and Longitudinal Datacited by 10
  12. 2023Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Modelscited by 9
35 more
  1. 2023Synthesize High-dimensional Longitudinal Electronic Health Records via Hierarchical Autoregressive Language Modelcited by 8
  2. 2025A Consensus Privacy Metrics Framework for Synthetic Datacited by 8
  3. 2025Synthetic Data Privacy Metricscited by 7
  4. 2023Collaborative Synthesis of Patient Records through Multi-Visit Health State Inferencecited by 6
  5. 2024FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with Contrastive Learning in Multimodal Electronic Health Recordscited by 6
  6. 2024SynEHRgy: Synthesizing Mixed-Type Structured Electronic Health Records using Decoder-Only Transformerscited by 5
  7. 2024TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Datacited by 4
  8. 2025Generating Clinically Realistic EHR Data via a Hierarchy- and Semantics-Guided Transformercited by 4
  9. 2023Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklistcited by 3
  10. 2025Synthetic Data Generation for Augmenting Small Samplescited by 3
  11. 2024IGNITE: Individualized GeNeration of Imputations in Time-series Electronic health recordscited by 2
  12. 2025Diffmv: A Unified Diffusion Framework for Healthcare Predictions with Random Missing Views and View Lazinesscited by 2
  13. 2025TabularQGAN: A quantum generative model for tabular data synthesiscited by 2
  14. 2025Patient-Zero: Scaling Synthetic Patient Agents to Real-World Distributions without Real Patient Datacited by 2
  15. 2026SynthAgent: A Multi-Agent LLM Framework for Realistic Patient Simulation -- A Case Study in Obesity with Mental Health Comorbiditiescited by 2
  16. 2023Methods for generating and evaluating synthetic longitudinal patient data: a systematic reviewcited by 1
  17. 2024Fairness-Optimized Synthetic EHR Generation for Arbitrary Downstream Predictive Taskscited by 1
  18. 2025CancerGUIDE: Cancer Guideline Understanding via Internal Disagreement Estimationcited by 1
  19. 2026From Statistical Fidelity to Clinical Consistency: Scalable Generation and Auditing of Synthetic Patient Trajectoriescited by 1
  20. 2023Joint Distributional Learning via Cramer-Wold Distancecited by 0
  21. 2024DE-CGAN: Boosting rTMS Treatment Prediction with Diversity Enhancing Conditional Generative Adversarial Networkscited by 0
  22. 2024TransFair: Transferring Fairness from Ocular Disease Classification to Progression Predictioncited by 0
  23. 2025Critical Challenges and Guidelines in Evaluating Synthetic Tabular Data: A Systematic Reviewcited by 0
  24. 2025Generating Multi-Table Time Series EHR from Latent Space with Minimal Preprocessingcited by 0
  25. 2025TCDiff: Triplex Cascaded Diffusion for High-fidelity Multimodal EHRs Generation with Incomplete Clinical Datacited by 0
  26. 2025Privacy-Preserving Tabular Synthetic Data Generation Using TabularARGNcited by 0
  27. 2025Enabling Granular Subgroup Level Model Evaluations by Generating Synthetic Medical Time Seriescited by 0
  28. 2026High-Fidelity Longitudinal Patient Simulation Using Real-World Datacited by 0
  29. 2026Evaluation metrics for temporal preservation in synthetic longitudinal patient datacited by 0
  30. 2026ESL-Bench: An Event-Driven Synthetic Longitudinal Benchmark for Health Agentscited by 0
  31. 2026FedEHR-Gen: Federated Synthetic Time-Series EHR Generation via Latent Space Alignment and Distribution-Aware Aggregationcited by 0
  32. 2026AdaPCLA: Adaptive Prior-Calibrated Logit Adjustment for Long-Tailed Longitudinal EHR Generationcited by 0
  33. 2026SynPre-FL: Synthetic data-driven pretraining integrated Federated Learning training frameworkcited by 0
  34. 2026Improving the Realism of Synthetic Clinical Benchmarks Under Utility Constraintscited by 0
  35. 2026Learning Continuous Patient Trajectories from Electronic Health Recordscited by 0