mlsanityData to 14 Oct 2026

Generating Multi-label Discrete Patient Records using Generative Adversarial Networks

arXiv 1703.06490 ↗First posted 19 Mar 2017cs.LG cs.NEcited by 197

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Abstract (arXiv)

Access to electronic health record (EHR) data has motivated computational advances in medical research. However, various concerns, particularly over privacy, can limit access to and collaborative use of EHR data. Sharing synthetic EHR data could mitigate risk. In this paper, we propose a new approach, medical Generative Adversarial Network (medGAN), to generate realistic synthetic patient records. Based on input real patient records, medGAN can generate high-dimensional discrete variables (e.g., binary and count features) via a combination of an autoencoder and generative adversarial networks. We also propose minibatch averaging to efficiently avoid mode collapse, and increase the learning efficiency with batch normalization and shortcut connections. To demonstrate feasibility, we showed that medGAN generates synthetic patient records that achieve comparable performance to real data on many experiments including distribution statistics, predictive modeling tasks and a medical expert review. We also empirically observe a limited privacy risk in both identity and attribute disclosure using medGAN.

Key facts. Cited by 197 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Mar 2017.

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Citing papers per year: 2021: 29; 2022: 31; 2023: 36; 2024: 41; 2025: 38; 2026: 22.

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  1. 2021Deep Neural Networks and Tabular Data: A Surveycited by 491
  2. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 321
  3. 2022Language Models are Realistic Tabular Data Generatorscited by 294
  4. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 159
  5. 2021CTAB-GAN: Effective Table Data Synthesizingcited by 156
  6. 2023Machine Learning for Synthetic Data Generation: A Reviewcited by 120
  7. 2023Membership Inference Attacks against Diffusion Modelscited by 86
  8. 2022CTAB-GAN+: Enhancing Tabular Data Synthesiscited by 70
  9. 2022Synthetic Data -- what, why and how?cited by 67
  10. 2023Generative Table Pre-training Empowers Models for Tabular Predictioncited by 55
  11. 2021Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applicationscited by 47
  12. 2021Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integrationcited by 44
38 more
  1. 2022Adversarial random forests for density estimation and generative modelingcited by 44
  2. 2022A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Modelscited by 43
  3. 2021Copula Flows for Synthetic Data Generationcited by 35
  4. 2021Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Reviewcited by 35
  5. 2021TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networkscited by 34
  6. 2023Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Datacited by 29
  7. 2024How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Datacited by 29
  8. 2024A Systematic Review on Long-Tailed Learningcited by 28
  9. 2025A Comprehensive Survey of Synthetic Tabular Data Generationcited by 28
  10. 2022TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Datacited by 27
  11. 2021Towards Generating Real-World Time Series Datacited by 26
  12. 2023Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Datacited by 26
  13. 2023Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Modelscited by 24
  14. 2023AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizingcited by 24
  15. 2024TWIN-GPT: Digital Twins for Clinical Trials via Large Language Modelcited by 24
  16. 2024Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challengescited by 24
  17. 2023MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Modelcited by 23
  18. 2021FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumoniacited by 22
  19. 2022PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learningcited by 22
  20. 2024CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelinescited by 22
  21. 2021Generative adversarial networks in time series: A survey and taxonomycited by 21
  22. 2023ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitizationcited by 20
  23. 2022The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithmscited by 19
  24. 2023EHRDiff: Exploring Realistic EHR Synthesis with Diffusion Modelscited by 19
  25. 2024MedSyn: LLM-based Synthetic Medical Text Generation Frameworkcited by 19
  26. 2023Synthesizing Mixed-type Electronic Health Records using Diffusion Modelscited by 18
  27. 2025Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraintscited by 18
  28. 2022On the Privacy Properties of GAN-generated Samplescited by 17
  29. 2022Multi-Label Clinical Time-Series Generation via Conditional GANcited by 16
  30. 2021Generative Adversarial Networks: A Survey Towards Private and Secure Applicationscited by 15
  31. 2022UDoc-GAN: Unpaired Document Illumination Correction with Background Light Priorcited by 15
  32. 2023Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospectscited by 15
  33. 2025A Survey on Deep Learning Approaches for Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, Diversity, and Beyondcited by 15
  34. 2024EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Modelscited by 14
  35. 2022A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sourcescited by 13
  36. 2023AI-Generated Images as Data Source: The Dawn of Synthetic Eracited by 13
  37. 2021Causal-TGAN: Generating Tabular Data Using Causal Generative Adversarial Networkscited by 12
  38. 2021The Devil is in the GAN: Backdoor Attacks and Defenses in Deep Generative Modelscited by 12

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