ArticleNature communications2022
A Multifaceted benchmarking of synthetic electronic health record generation models.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
47 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Synthetic data generation methods for longitudinal and time series health data: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Applications and Concerns of ChatGPT and Other Conversational Large Language Models in Health Care: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Timesynth: A Temporal Fidelity Framework for Health Signal Digital Twins.Research square · 2026Article
- Generating synthetic multi-national longitudinal cohorts for clinically grounded HIV research.Nature communications · 2026Article
- High-Fidelity Synthetic Data Replicates Clinical Prediction Performance in a Million-Patient Diabetes Cohort.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A Foundation Model for Capturing Complexity of Menstrual Health Data.npj women's health · 2026Article
- Reward-Guided Generation Improves the Scientific Utility of Synthetic Biomedical Data.medRxiv : the preprint server for health sciences · 2026Article
- Methods for Generating and Evaluating Synthetic Longitudinal Patient Data: A Systematic Review.Journal of healthcare informatics research · 2026Review
- An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences.NAR genomics and bioinformatics · 2026Article
- 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
- Protecting patient privacy in tabular synthetic health data: a regulatory perspective.NPJ digital medicine · 2025Review
- Data augmentation alters feature importance in XGBoost for CVD prediction.Scientific reports · 2025Article
- Generating Synthetic Multi-national Longitudinal Cohorts for Clinically Grounded HIV Research.medRxiv : the preprint server for health sciences · 2025Article
- Longitudinal Synthetic Data Generation by Artificial Intelligence to Accelerate Clinical and Translational Research in Breast Cancer.JCO clinical cancer informatics · 2025Article
- A consensus privacy metrics framework for synthetic data.Patterns (New York, N.Y.) · 2025Article
- Fidelity-agnostic synthetic data generation improves utility while retaining privacy.Patterns (New York, N.Y.) · 2025Article
- Artificial Intelligence Large Language Models in Cardiology.Reviews in cardiovascular medicine · 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
- How good is your synthetic data? SynthRO, a dashboard to evaluate and benchmark synthetic tabular data.BMC medical informatics and decision making · 2025Article
- Preserving information while respecting privacy through an information theoretic framework for synthetic health data generation.NPJ digital medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Synthetic health data have the potential to mitigate privacy concerns in supporting biomedical research and healthcare applications. Modern approaches for data generation 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 systematic benchmarking framework to appraise key characteristics with respect to utility and privacy metrics. We apply the framework to evaluate synthetic data generation methods for electronic health records 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 health data and 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.
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What OpenQuestion 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 OpenQuestion graph.