ArticleNPJ digital medicine2023
Synthetic electronic health records generated with variational graph autoencoders.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Synthetic data generation methods for longitudinal and time series health data: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- An integrated evaluation framework for synthetic clinical data in severely imbalanced settings: fidelity, privacy-risk profiling, and diagnostic utility.BMC medical informatics and decision making · 2026Article
- Research progress and challenges of multimodal deep learning models for prognosis of gastric cancer.Discover oncology · 2026Review
- Methods for Generating and Evaluating Synthetic Longitudinal Patient Data: A Systematic Review.Journal of healthcare informatics research · 2026Review
- Leveraging generative AI to enhance Synthea model development.JAMIA open · 2026Article
- Augmenting small tabular health data for training prognostic ensemble machine learning models using generative models.BMC medical informatics and decision making · 2025Article
- Clinical evaluation of medical image synthesis: a case study in wireless capsule endoscopy.Scientific reports · 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
- QAMT: An LLM-Based Framework for Quality-Assured Medical Time-Series Data Generation.Sensors (Basel, Switzerland) · 2025Article
- Synthetic data and health equity: accounting for racism and sexism in health care delivery.Health affairs scholar · 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
- Anonymize or synthesize? Privacy-preserving methods for heart failure score analytics.European heart journal. Digital health · 2025Article
- Advancing rare disease therapeutics through digital twins: Opportunities in drug development and precision dosing.Computational and structural biotechnology journal · 2025Review
- Decades in the Making: The Evolution of Digital Health Research Infrastructure Through Synthetic Data, Common Data Models, and Federated Learning.Journal of medical Internet research · 2024Article
- Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist.The Lancet. Digital health · 2024Article
- Genetics-driven risk predictions leveraging the Mendelian randomization framework.Genome research · 2024Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Data-driven medical care delivery must always respect patient privacy-a requirement that is not easily met. This issue has impeded improvements to healthcare software and has delayed the long-predicted prevalence of artificial intelligence in healthcare. Until now, it has been very difficult to share data between healthcare organizations, resulting in poor statistical models due to unrepresentative patient cohorts. Synthetic data, i.e., artificial but realistic electronic health records, could overcome the drought that is troubling the healthcare sector. Deep neural network architectures, in particular, have shown an incredible ability to learn from complex data sets and generate large amounts of unseen data points with the same statistical properties as the training data. Here, we present a generative neural network model that can create synthetic health records with realistic timelines. These clinical trajectories are generated on a per-patient basis and are represented as linear-sequence graphs of clinical events over time. We use a variational graph autoencoder (VGAE) to generate synthetic samples from real-world electronic health records. Our approach generates health records not seen in the training data. We show that these artificial patient trajectories are realistic and preserve patient privacy and can therefore support the safe sharing of data across organizations.
Identifiers
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.