ArticleJournal of the American Medical Informatics Association : JAMIA2021
Application of Bayesian networks to generate synthetic health data.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Leveraging Synthetic Clinical Data for Validation and Operational Readiness in Clinical Trials.Healthcare (Basel, Switzerland) · 2026Article
- An Evaluation of Pretrained Generative Models for Augmenting Small Health Data: Comparative Modeling Study.Journal of medical Internet research · 2026Article
- Artificial Intelligence in Rare Diseases: Workflow-Integrated Precision Kidney Care.Clinics and practice · 2026Review
- Sample size requirements for machine learning classification of binary outcomes in bulk RNA-Seq data.BMC bioinformatics · 2026Article
- Should we synthesize more than we need: impact of synthetic data generation for high-dimensional cross-sectional medical data.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Augmenting small tabular health data for training prognostic ensemble machine learning models using generative models.BMC medical informatics and decision making · 2025Article
- Fidelity-agnostic synthetic data generation improves utility while retaining privacy.Patterns (New York, N.Y.) · 2025Article
- Magnitude and Impact of Hallucinations in Tabular Synthetic Health Data on Prognostic Machine Learning Models: Validation Study.Journal of medical Internet research · 2025Article
- 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
- A review on generative AI models for synthetic medical text, time series, and longitudinal data.NPJ digital medicine · 2025Article
- Article
- Augmenting Insufficiently Accruing Oncology Clinical Trials Using Generative Models: Validation Study.Journal of medical Internet research · 2025Article
- Review
- On the evaluation of synthetic longitudinal electronic health records.BMC medical research methodology · 2024Article
- Comparison of Synthetic Data Generation Techniques for Control Group Survival Data in Oncology Clinical Trials: Simulation Study.JMIR medical informatics · 2024Article
- Synthetic Data Improve Survival Status Prediction Models in Early-Onset Colorectal Cancer.JCO clinical cancer informatics · 2024Article
- New Approach for Generating Synthetic Medical Data to Predict Type 2 Diabetes.Bioengineering (Basel, Switzerland) · 2023Article
- Ten Topics to Get Started in Medical Informatics Research.Journal of medical Internet research · 2023Article
- Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges.Journal of medical Internet research · 2023Review
- Synthetic electronic health records generated with variational graph autoencoders.NPJ digital medicine · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study seeks to develop a fully automated method of generating synthetic data from a real dataset that could be employed by medical organizations to distribute health data to researchers, reducing the need for access to real data. We hypothesize the application of Bayesian networks will improve upon the predominant existing method, medBGAN, in handling the complexity and dimensionality of healthcare data. MATERIALS AND
methodsWe employed Bayesian networks to learn probabilistic graphical structures and simulated synthetic patient records from the learned structure. We used the University of California Irvine (UCI) heart disease and diabetes datasets as well as the MIMIC-III diagnoses database. We evaluated our method through statistical tests, machine learning tasks, preservation of rare events, disclosure risk, and the ability of a machine learning classifier to discriminate between the real and synthetic data.
resultsOur Bayesian network model outperformed or equaled medBGAN in all key metrics. Notable improvement was achieved in capturing rare variables and preserving association rules. DISCUSSION: Bayesian networks generated data sufficiently similar to the original data with minimal risk of disclosure, while offering additional transparency, computational efficiency, and capacity to handle more data types in comparison to existing methods. We hope this method will allow healthcare organizations to efficiently disseminate synthetic health data to researchers, enabling them to generate hypotheses and develop analytical tools.
conclusionWe conclude the application of Bayesian networks is a promising option for generating realistic synthetic health data that preserves the features of the original data without compromising data privacy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.