ArticleJAMIA open2021
Evaluating the utility of synthetic COVID-19 case data.
Article in JAMIA open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Synthetic Medical Data vs. The Four Principles: Ethical Trade-Offs and Zombie Data.Health care analysis : HCA : journal of health philosophy and policy · 2026Article
- Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.Annual review of biomedical data science · 2026Review
- Synthetic health data in Canada: A scoping review of methods, applications, and data sources.Canadian journal of public health = Revue canadienne de sante publique · 2026Article
- A consensus privacy metrics framework for synthetic data.Patterns (New York, N.Y.) · 2025Article
- Comparison of Three Anonymization Tools for a Health Fitness Study.Scientific data · 2025Article
- Denoising diffusion model for increased performance of detecting structural heart disease.medRxiv : the preprint server for health sciences · 2024Article
- An evaluation of the replicability of analyses using synthetic health data.Scientific reports · 2024Article
- Mimicking clinical trials with synthetic acute myeloid leukemia patients using generative artificial intelligence.NPJ digital medicine · 2024Article
- Brain tumor segmentation using synthetic MR images - A comparison of GANs and diffusion models.Scientific data · 2024Article
- A comparison of synthetic data generation and federated analysis for enabling international evaluations of cardiovascular health.Scientific reports · 2023Article
- Generating synthetic mixed-type longitudinal electronic health records for artificial intelligent applications.NPJ digital medicine · 2023Article
- Validating a membership disclosure metric for synthetic health data.JAMIA open · 2022Article
- Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: results from analyzing >1.8 million SARS-CoV-2 tests in the United States National COVID Cohort Collaborative (N3C).Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Generating high-fidelity synthetic time-to-event datasets to improve data transparency and accessibility.BMC medical research methodology · 2022Article
- Utility Metrics for Evaluating Synthetic Health Data Generation Methods: Validation Study.JMIR medical informatics · 2022Article
- Reconciling public health common good and individual privacy: new methods and issues in geoprivacy.International journal of health geographics · 2022Article
- The Problem of Fairness in Synthetic Healthcare Data.Entropy (Basel, Switzerland) · 2021Article
- Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: Results from analyzing >1.8 million SARS-CoV-2 tests in the United States National COVID Cohort Collaborative (N3C).medRxiv : the preprint server for health sciences · 2021Article
- Synthetic data in machine learning for medicine and healthcare.Nature biomedical engineering · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundConcerns about patient privacy have limited access to COVID-19 datasets. Data synthesis is one approach for making such data broadly available to the research community in a privacy protective manner.
objectivesEvaluate the utility of synthetic data by comparing analysis results between real and synthetic data.
methodsA gradient boosted classification tree was built to predict death using Ontario's 90 514 COVID-19 case records linked with community comorbidity, demographic, and socioeconomic characteristics. Model accuracy and relationships were evaluated, as well as privacy risks. The same model was developed on a synthesized dataset and compared to one from the original data.
resultsThe AUROC and AUPRC for the real data model were 0.945 [95% confidence interval (CI), 0.941-0.948] and 0.34 (95% CI, 0.313-0.368), respectively. The synthetic data model had AUROC and AUPRC of 0.94 (95% CI, 0.936-0.944) and 0.313 (95% CI, 0.286-0.342) with confidence interval overlap of 45.05% and 52.02% when compared with the real data. The most important predictors of death for the real and synthetic models were in descending order: age, days since January 1, 2020, type of exposure, and gender. The functional relationships were similar between the two data sets. Attribute disclosure risks were 0.0585, and membership disclosure risk was low.
conclusionsThis synthetic dataset could be used as a proxy for the real dataset.
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