ArticleBMC medical research methodology2020
Generation and evaluation of synthetic patient data.
Article in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 papers, 2 of them syntheses that pooled it.
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Who cites it
114 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review.BMC medical informatics and decision making · 2023Pooled it
- Adjustment for Baseline Covariates to Increase Efficiency in RCTs with Binary Endpoint: A Comparison of Bayesian and Frequentist Approaches.International journal of environmental research and public health · 2021Trial
- A global digital navigator of human health for precision medicine.Nature medicine · 2026Article
- Leveraging Synthetic Clinical Data for Validation and Operational Readiness in Clinical Trials.Healthcare (Basel, Switzerland) · 2026Article
- Synthetic Medical Data vs. The Four Principles: Ethical Trade-Offs and Zombie Data.Health care analysis : HCA : journal of health philosophy and policy · 2026Article
- Validating methods for inferring co-occurring diseases: a flexible framework for simulating synthetic data.BMC medical research methodology · 2026Article
- Large language models for generating longitudinal synthetic data in low-risk pregnancy care.Data in brief · 2026Article
- Quality assessment of synthetic data in healthcare: a critical appraisal with a focus on subgroup bias.BMC medical informatics and decision making · 2026Article
- 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
- Investigating Leptomeningeal Anastomoses' Role in Collateral Blood Flow using SPECT and 4D Flow MRI.Annals of biomedical engineering · 2026Article
- 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
- Selecting synthetic data for successful simulation-based transfer learning in dynamical biological systems.BMC bioinformatics · 2026Article
- High-Fidelity Synthetic Data Replicates Clinical Prediction Performance in a Million-Patient Diabetes Cohort.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- ANMCO position paper E-health integration in cardiology clinical care framework.European heart journal supplements : journal of the European Society of Cardiology · 2026Article
- Rethinking Trust in Synthetic Health Data: Lessons From 7 European Research Initiatives.Journal of medical Internet research · 2026Article
- A simulation-based methodological analysis of caesarean-section patterns in women of reproductive age in Saudi Arabia.Scientific reports · 2026Article
- Smart Monitoring for Cancer Treatment: Feasibility Study of an IoT-Based Assessment System.Sensors (Basel, Switzerland) · 2026Article
- Who's afraid of synthetic data? Hybrid approaches to deliver medical digital twins.Informatics in medicine unlocked · 2026Article
54 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning (ML) has made a significant impact in medicine and cancer research; however, its impact in these areas has been undeniably slower and more limited than in other application domains. A major reason for this has been the lack of availability of patient data to the broader ML research community, in large part due to patient privacy protection concerns. High-quality, realistic, synthetic datasets can be leveraged to accelerate methodological developments in medicine. By and large, medical data is high dimensional and often categorical. These characteristics pose multiple modeling challenges.
methodsIn this paper, we evaluate three classes of synthetic data generation approaches; probabilistic models, classification-based imputation models, and generative adversarial neural networks. Metrics for evaluating the quality of the generated synthetic datasets are presented and discussed.
resultsWhile the results and discussions are broadly applicable to medical data, for demonstration purposes we generate synthetic datasets for cancer based on the publicly available cancer registry data from the Surveillance Epidemiology and End Results (SEER) program. Specifically, our cohort consists of breast, respiratory, and non-solid cancer cases diagnosed between 2010 and 2015, which includes over 360,000 individual cases.
conclusionsWe discuss the trade-offs of the different methods and metrics, providing guidance on considerations for the generation and usage of medical synthetic data.
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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.