ArticleNPJ digital medicine2020
Generating high-fidelity synthetic patient data for assessing machine learning healthcare software.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 1 of them a synthesis that pooled it.
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Who cites it
76 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Translation readiness of model-based synthetic tabular data in healthcare: a systematic review and governance audit.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.Annual review of biomedical data science · 2026Review
- Tree-based machine learning models recover rule-based flare labels from virtual symptom diaries in pediatric Behçet's disease: an in silico proof-of-concept study.Rheumatology international · 2026Article
- Big Data and Trustworthy AI for Heart Failure: A Review.Circulation. Heart failure · 2026Review
- 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
- Beyond classical models: LLM-driven survival analysis for breast cancer prognosis using European cancer registry data.BMC medical informatics and decision making · 2026Article
- Artificial Intelligence in Rare Diseases: Workflow-Integrated Precision Kidney Care.Clinics and practice · 2026Review
- Measuring the gap: correlating synthetic-to-real drift with PHI de-identification performance.Genomics & informatics · 2026Article
- A leakage-controlled machine learning framework for postprandial triglyceride phenotyping using synthetic clinical data.Scientific reports · 2026Article
- Generative Artificial Intelligence Transitions Pharmaceutical Development from Empirical Screening to Predictive Molecular Design and Clinical Trial Optimization.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Evaluation of Cerebral Blood Flow and Cerebral Autoregulation Using Synthetic Data and In Silico Modeling.CNS neuroscience & therapeutics · 2026Article
- Graph-Convolutional-Beta-VAE for synthetic abdominal aortic aneurysm generation.Medical & biological engineering & computing · 2026Article
- Leveraging generative AI to enhance Synthea model development.JAMIA open · 2026Article
- A novel pipeline for realistic synthetic longitudinal EHR data generation.Research square · 2026Article
- Federated causal discovery in medicine: trends, opportunities, and challenges.Frontiers in digital health · 2026Article
- Directional drift in biologically meaningful vector planes: A proposed geometric framework for early detection of subthreshold disease.PloS one · 2026Article
- Synthetic data in rheumatology: a systematic literature review.Rheumatology advances in practice · 2026Review
- Crossing borders securely: synthetic data and federated networks for privacy-preserving access to real-world data and emerging use cases.NPJ digital medicine · 2025Review
- Artificial Intelligence for Cardiovascular Care in Action: From Learning to Implementation in Health Systems.JACC. Advances · 2025Review
- A consensus privacy metrics framework for synthetic data.Patterns (New York, N.Y.) · 2025Article
16 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is a growing demand for the uptake of modern artificial intelligence technologies within healthcare systems. Many of these technologies exploit historical patient health data to build powerful predictive models that can be used to improve diagnosis and understanding of disease. However, there are many issues concerning patient privacy that need to be accounted for in order to enable this data to be better harnessed by all sectors. One approach that could offer a method of circumventing privacy issues is the creation of realistic synthetic data sets that capture as many of the complexities of the original data set (distributions, non-linear relationships, and noise) but that does not actually include any real patient data. While previous research has explored models for generating synthetic data sets, here we explore the integration of resampling, probabilistic graphical modelling, latent variable identification, and outlier analysis for producing realistic synthetic data based on UK primary care patient data. In particular, we focus on handling missingness, complex interactions between variables, and the resulting sensitivity analysis statistics from machine learning classifiers, while quantifying the risks of patient re-identification from synthetic datapoints. We show that, through our approach of integrating outlier analysis with graphical modelling and resampling, we can achieve synthetic data sets that are not significantly different from original ground truth data in terms of feature distributions, feature dependencies, and sensitivity analysis statistics when inferring machine learning classifiers. What is more, the risk of generating synthetic data that is identical or very similar to real patients is shown to be low.
Identifiers
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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.