ArticleJournal of the American Medical Informatics Association : JAMIA2019
Synthesizing electronic health records using improved generative adversarial networks.
Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 1 of them a synthesis that pooled it.
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Who cites it
74 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.Frontiers in digital health · 2025Pooled it
- Addressing the Challenges in Using Synthetic Data for Health Research: Application to Cardiology.JMIR cardio · 2026Review
- Review
- Ethical Considerations in Patient Privacy and Data Handling for AI in Cardiovascular Imaging and Radiology.Journal of imaging informatics in medicine · 2026Review
- Anonymization and visualization of health data and biomarkers.NPJ digital medicine · 2026Article
- High-Fidelity Synthetic Data Replicates Clinical Prediction Performance in a Million-Patient Diabetes Cohort.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Deep generative hidden Markov models for synthetic patient data generation: a novel approach for medical AI research.BMC medical informatics and decision making · 2026Article
- Leveraging generative AI to enhance Synthea model development.JAMIA open · 2026Article
- A novel pipeline for realistic synthetic longitudinal EHR data generation.Research square · 2026Article
- An empirical evaluation of dimensionality reduction and class balancing for medical text classification.Scientific reports · 2025Article
- Predictive framework for cervical cancer brachytherapy fractionation mode integrating generative model and dynamic feature aggregation GNNs.BMC medical informatics and decision making · 2025Article
- Data augmentation alters feature importance in XGBoost for CVD prediction.Scientific reports · 2025Article
- QAMT: An LLM-Based Framework for Quality-Assured Medical Time-Series Data Generation.Sensors (Basel, Switzerland) · 2025Article
- Generative artificial intelligence in diabetes healthcare.iScience · 2025Review
- Generating synthetic electronic health record data: a methodological scoping review with benchmarking on phenotype data and open-source software.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- MediSim: Multi-granular simulation for enriching longitudinal, multi-modal electronic health records.Patterns (New York, N.Y.) · 2025Article
- A review on generative AI models for synthetic medical text, time series, and longitudinal data.NPJ digital medicine · 2025Article
- Artificial Intelligence and Predictive Modeling in the Management and Treatment of Episodic Migraine.Current pain and headache reports · 2025Review
- Preserving information while respecting privacy through an information theoretic framework for synthetic health data generation.NPJ digital medicine · 2025Article
- Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives.Current drug targets · 2025Review
14 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The aim of this study was to generate synthetic electronic health records (EHRs). The generated EHR data will be more realistic than those generated using the existing medical Generative Adversarial Network (medGAN) method. Materials and Methods: We modified medGAN to obtain two synthetic data generation models-designated as medical Wasserstein GAN with gradient penalty (medWGAN) and medical boundary-seeking GAN (medBGAN)-and compared the results obtained using the three models. We used 2 databases: MIMIC-III and National Health Insurance Research Database (NHIRD), Taiwan. First, we trained the models and generated synthetic EHRs by using these three 3 models. We then analyzed and compared the models' performance by using a few statistical methods (Kolmogorov-Smirnov test, dimension-wise probability for binary data, and dimension-wise average count for count data) and 2 machine learning tasks (association rule mining and prediction). Results: We conducted a comprehensive analysis and found our models were adequately efficient for generating synthetic EHR data. The proposed models outperformed medGAN in all cases, and among the 3 models, boundary-seeking GAN (medBGAN) performed the best. Discussion: To generate realistic synthetic EHR data, the proposed models will be effective in the medical industry and related research from the viewpoint of providing better services. Moreover, they will eliminate barriers including limited access to EHR data and thus accelerate research on medical informatics. Conclusion: The proposed models can adequately learn the data distribution of real EHRs and efficiently generate realistic synthetic EHRs. The results show the superiority of our models over the existing model.
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