# A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Models

arXiv: https://arxiv.org/abs/2208.01230 · HTML: https://mlsanity.com/paper/2208.01230/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 78 references that are in the map.
- First posted on arXiv 2 Aug 2022.

## Builds on (most cited first, 15 of 15)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [Differentially Private Generative Adversarial Network](https://mlsanity.com/paper/1802.06739/) (2018)
- [Generating Multi-label Discrete Patient Records using Generative Adversarial Networks](https://mlsanity.com/paper/1703.06490/) (2017)
- [Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification](https://mlsanity.com/paper/1801.02385/) (2018)
- [CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection](https://mlsanity.com/paper/2103.05094/) (2021)
- [Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applications](https://mlsanity.com/paper/2112.12047/) (2021)
- [Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records](https://mlsanity.com/paper/1709.01648/) (2017)
- [Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluation](https://mlsanity.com/paper/2012.03769/) (2020)
- [Generating Electronic Health Records with Multiple Data Types and Constraints](https://mlsanity.com/paper/2003.07904/) (2020)
- [Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models](https://mlsanity.com/paper/2006.05331/) (2020)
- [Generating Multi-Categorical Samples with Generative Adversarial Networks](https://mlsanity.com/paper/1807.01202/) (2018)
- [Going Digital: A Survey on Digitalization and Large Scale Data Analytics in Healthcare](https://mlsanity.com/paper/1606.08075/) (2016)

## Built on by (most cited first, 30 of 43)

- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [Differentially Private Diffusion Models Generate Useful Synthetic Images](https://mlsanity.com/paper/2302.13861/) (2023)
- [QoQ-Med: Building Multimodal Clinical Foundation Models with Domain-Aware GRPO Training](https://mlsanity.com/paper/2506.00711/) (2025)
- [SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Data](https://mlsanity.com/paper/2404.15821/) (2024)
- [TSGBench: Time Series Generation Benchmark](https://mlsanity.com/paper/2309.03755/) (2023)
- [Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Models](https://mlsanity.com/paper/2310.15290/) (2023)
- [CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelines](https://mlsanity.com/paper/2402.04400/) (2024)
- [EHRDiff: Exploring Realistic EHR Synthesis with Diffusion Models](https://mlsanity.com/paper/2303.05656/) (2023)
- [Synthesizing Mixed-type Electronic Health Records using Diffusion Models](https://mlsanity.com/paper/2302.14679/) (2023)
- [Can I trust my fake data -- A comprehensive quality assessment framework for synthetic tabular data in healthcare](https://mlsanity.com/paper/2401.13716/) (2024)
- [Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIV](https://mlsanity.com/paper/2208.08655/) (2022)
- [Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey](https://mlsanity.com/paper/2209.09239/) (2022)
- [Synthesize High-dimensional Longitudinal Electronic Health Records via Hierarchical Autoregressive Language Model](https://mlsanity.com/paper/2304.02169/) (2023)
- [Synthetic Data in Healthcare](https://mlsanity.com/paper/2304.03243/) (2023)
- [Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Data](https://mlsanity.com/paper/2310.19250/) (2023)
- [Synthetic Data Privacy Metrics](https://mlsanity.com/paper/2501.03941/) (2025)
- [A Consensus Privacy Metrics Framework for Synthetic Data](https://mlsanity.com/paper/2503.04980/) (2025)
- [Guided Discrete Diffusion for Electronic Health Record Generation](https://mlsanity.com/paper/2404.12314/) (2024)
- [CEHR-XGPT: A Scalable Multi-Task Foundation Model for Electronic Health Records](https://mlsanity.com/paper/2509.03643/) (2025)
- [Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference](https://mlsanity.com/paper/2510.18768/) (2025)
- [Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist](https://mlsanity.com/paper/2311.02107/) (2023)
- [Debiasing Synthetic Data Generated by Deep Generative Models](https://mlsanity.com/paper/2411.04216/) (2024)
- [Methods for generating and evaluating synthetic longitudinal patient data: a systematic review](https://mlsanity.com/paper/2309.12380/) (2023)
- [SSSD-ECG-nle: New Label Embeddings with Structured State-Space Models for ECG generation](https://mlsanity.com/paper/2407.11108/) (2024)
- [Zero-shot generation of synthetic neurosurgical data with large language models](https://mlsanity.com/paper/2502.09566/) (2025)
- [PROVCREATOR: Synthesizing Complex Heterogenous Graphs with Node and Edge Attributes](https://mlsanity.com/paper/2507.20967/) (2025)
- [CancerGUIDE: Cancer Guideline Understanding via Internal Disagreement Estimation](https://mlsanity.com/paper/2509.07325/) (2025)
- [SurvDiff: A Diffusion Model for Generating Synthetic Data in Survival Analysis](https://mlsanity.com/paper/2509.22352/) (2025)
- [Permissioned Blockchain-based Framework for Ranking Synthetic Data Generators](https://mlsanity.com/paper/2405.07196/) (2024)
- [Generating Multi-Table Time Series EHR from Latent Space with Minimal Preprocessing](https://mlsanity.com/paper/2507.06996/) (2025)

## 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.