ArticleScientific reports2023
A comparison of synthetic data generation and federated analysis for enabling international evaluations of cardiovascular health.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Addressing the Challenges in Using Synthetic Data for Health Research: Application to Cardiology.JMIR cardio · 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
- Selecting medical research data platforms for translational biomedical research: a five-tier overview and requirement-weighted assessment framework.Frontiers in digital health · 2026Review
- Federated analytics for non-communicable disease surveillance in the European health data space: a scoping review and conceptual framework.Frontiers in public health · 2026Article
- Crossing borders securely: synthetic data and federated networks for privacy-preserving access to real-world data and emerging use cases.NPJ digital medicine · 2025Review
- Enhancing Developmental Language Disorder Identification with Artificial Intelligence: Development of an Explainable Screening App Using Real and Synthetic Data.Journal of autism and developmental disorders · 2025Article
- Privacy-by-Design Approach to Generate Two Virtual Clinical Trials for Multiple Sclerosis and Release Them as Open Datasets: Evaluation Study.Journal of medical Internet research · 2025Article
- Synthetic Tabular Data Generation Under Horizontal Federated Learning Environments in Acute Myeloid Leukemia: Case-Based Simulation Study.JMIR medical informatics · 2025Article
- How good is your synthetic data? SynthRO, a dashboard to evaluate and benchmark synthetic tabular data.BMC medical informatics and decision making · 2025Article
- New ways to use imaging data in cardiovascular research: survey of opinions on federated learning and synthetic data.European heart journal. Imaging methods and practice · 2025Article
- Bridging borders: Current trends and future directions in comparative health systems research.Health services research · 2024Article
- Comparison of Synthetic Data Generation Techniques for Control Group Survival Data in Oncology Clinical Trials: Simulation Study.JMIR medical informatics · 2024Article
- An evaluation of the replicability of analyses using synthetic health data.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sharing health data for research purposes across international jurisdictions has been a challenge due to privacy concerns. Two privacy enhancing technologies that can enable such sharing are synthetic data generation (SDG) and federated analysis, but their relative strengths and weaknesses have not been evaluated thus far. In this study we compared SDG with federated analysis to enable such international comparative studies. The objective of the analysis was to assess country-level differences in the role of sex on cardiovascular health (CVH) using a pooled dataset of Canadian and Austrian individuals. The Canadian data was synthesized and sent to the Austrian team for analysis. The utility of the pooled (synthetic Canadian + real Austrian) dataset was evaluated by comparing the regression results from the two approaches. The privacy of the Canadian synthetic data was assessed using a membership disclosure test which showed an F1 score of 0.001, indicating low privacy risk. The outcome variable of interest was CVH, calculated through a modified CANHEART index. The main and interaction effect parameter estimates of the federated and pooled analyses were consistent and directionally the same. It took approximately one month to set up the synthetic data generation platform and generate the synthetic data, whereas it took over 1.5 years to set up the federated analysis system. Synthetic data generation can be an efficient and effective tool for enabling multi-jurisdictional studies while addressing privacy concerns.
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What Socratic holds
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 Socratic graph.