ArticleJMIR medical informatics2020
Analyzing Medical Research Results Based on Synthetic Data and Their Relation to Real Data Results: Systematic Comparison From Five Observational Studies.
Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
- Linguistic Fidelity and Classification Performance of Large Language Models for Generating Synthetic Operative Notes: Evaluation Study.JMIR formative research · 2026Article
- Developing Synthetic Orthopantomogram Datasets Through Generative Models.International dental journal · 2026Article
- Knowledge Graph-Driven AI in Biohealth: From Biomedical Discovery to Health Risk Prediction.Delaware journal of public health · 2026Article
- A prediction model for childhood obesity risk based on maternal thyroid status and related parameters using machine learning: a mother-newborn-offspring study in a mild-to-moderate iodine deficiency area.International journal of obesity (2005) · 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
- A consensus privacy metrics framework for synthetic data.Patterns (New York, N.Y.) · 2025Article
- Medical data sharing and synthetic clinical data generation - maximizing biomedical resource utilization and minimizing participant re-identification risks.NPJ digital medicine · 2025Article
- Treatment disparities and prognostic implications in octogenarians versus non-octogenarians with high-gradient severe aortic stenosis.Open heart · 2025Article
- A machine learning approach for diagnostic and prognostic predictions, key risk factors and interactions.Health services & outcomes research methodology · 2025Article
- Differences in technical and clinical perspectives on AI validation in cancer imaging: mind the gap!European radiology experimental · 2025Article
- 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
- Review
- Actionability of Synthetic Data in a Heterogeneous and Rare Health Care Demographic: Adolescents and Young Adults With Cancer.JCO clinical cancer informatics · 2024Article
- Outcomes of ED chest pain visits: the prognostic value of negative but measurable high-sensitivity cardiac troponin (hs-cTn) levels.BMC emergency medicine · 2024Article
- Trends in Patient Characteristics and Cardiothoracic Surgeries over 14 Years (2010-2023): A Single Center Experience.Journal of clinical medicine · 2024Article
- Early Detection of Pulmonary Embolism in a General Patient Population Immediately Upon Hospital Admission Using Machine Learning to Identify New, Unidentified Risk Factors: Model Development Study.Journal of medical Internet research · 2024Article
- The CRP troponin test (CTT) stratifies mortality risk in patients with non-ST elevation myocardial infarction (NSTEMI).Clinical cardiology · 2024Article
- An evaluation of the replicability of analyses using synthetic health data.Scientific reports · 2024Article
- Circadian immunity from bench to bedside: a practical guide.The Journal of clinical investigation · 2024Review
- Synthetic data in cancer and cerebrovascular disease research: A novel approach to big data.PloS one · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrivacy restrictions limit access to protected patient-derived health information for research purposes. Consequently, data anonymization is required to allow researchers data access for initial analysis before granting institutional review board approval. A system installed and activated at our institution enables synthetic data generation that mimics data from real electronic medical records, wherein only fictitious patients are listed.
objectiveThis paper aimed to validate the results obtained when analyzing synthetic structured data for medical research. A comprehensive validation process concerning meaningful clinical questions and various types of data was conducted to assess the accuracy and precision of statistical estimates derived from synthetic patient data.
methodsA cross-hospital project was conducted to validate results obtained from synthetic data produced for five contemporary studies on various topics. For each study, results derived from synthetic data were compared with those based on real data. In addition, repeatedly generated synthetic datasets were used to estimate the bias and stability of results obtained from synthetic data.
resultsThis study demonstrated that results derived from synthetic data were predictive of results from real data. When the number of patients was large relative to the number of variables used, highly accurate and strongly consistent results were observed between synthetic and real data. For studies based on smaller populations that accounted for confounders and modifiers by multivariate models, predictions were of moderate accuracy, yet clear trends were correctly observed.
conclusionsThe use of synthetic structured data provides a close estimate to real data results and is thus a powerful tool in shaping research hypotheses and accessing estimated analyses, without risking patient privacy. Synthetic data enable broad access to data (eg, for out-of-organization researchers), and rapid, safe, and repeatable analysis of data in hospitals or other health organizations where patient privacy is a primary value.
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