ArticlePatterns (New York, N.Y.)2024
An evaluation of synthetic data augmentation for mitigating covariate bias in health data.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Exploring Bias in Medical Applications of Large Language Models: Protocol for a Systematic Review.JMIR research protocols · 2026Article
- Artificial intelligence-generated synthetic data for cancer research and clinical trials.Nature reviews. Cancer · 2026Review
- A Foundation Model for Capturing Complexity of Menstrual Health Data.npj women's health · 2026Article
- Model-based algorithms to ascertain smoking in administrative health data: a registry-based validation study.BMC medical research methodology · 2026Article
- Reward-Guided Generation Improves the Scientific Utility of Synthetic Biomedical Data.medRxiv : the preprint server for health sciences · 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
- Addressing implicit bias in pharmacy: strategies for equitable patient care and improved medication outcomes.Frontiers in public health · 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
- Augmenting small tabular health data for training prognostic ensemble machine learning models using generative models.BMC medical informatics and decision making · 2025Article
- Magnitude and Impact of Hallucinations in Tabular Synthetic Health Data on Prognostic Machine Learning Models: Validation Study.Journal of medical Internet research · 2025Article
- Generative AI mitigates representation bias and improves model fairness through synthetic health data.PLoS computational biology · 2025Article
- Article
- Augmenting Insufficiently Accruing Oncology Clinical Trials Using Generative Models: Validation Study.Journal of medical Internet research · 2025Article
- Improving medical machine learning models with generative balancing for equity and excellence.NPJ digital medicine · 2025Article
- Generative Artificial Intelligence for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations: An ISPOR Working Group Report.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2025Review
- Clinical Algorithms and the Legacy of Race-Based Correction: Historical Errors, Contemporary Revisions and Equity-Oriented Methodologies for Epidemiologists.Clinical epidemiology · 2025Review
- On the evaluation of synthetic longitudinal electronic health records.BMC medical research methodology · 2024Article
- Enhancement of Fairness in AI for Chest X-ray Classification.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Evaluating the Efficacy of Large Language Models for Dizzy History Taking and Peripheral Vestibular Disorder Diagnosis.Journal of otolaryngology - head & neck surgery = Le Journal d'oto-rhino-laryngologie et de chirurgie cervico-facialeArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data bias is a major concern in biomedical research, especially when evaluating large-scale observational datasets. It leads to imprecise predictions and inconsistent estimates in standard regression models. We compare the performance of commonly used bias-mitigating approaches (resampling, algorithmic, and post hoc approaches) against a synthetic data-augmentation method that utilizes sequential boosted decision trees to synthesize under-represented groups. The approach is called synthetic minority augmentation (SMA). Through simulations and analysis of real health datasets on a logistic regression workload, the approaches are evaluated across various bias scenarios (types and severity levels). Performance was assessed based on area under the curve, calibration (Brier score), precision of parameter estimates, confidence interval overlap, and fairness. Overall, SMA produces the closest results to the ground truth in low to medium bias (50% or less missing proportion). In high bias (80% or more missing proportion), the advantage of SMA is not obvious, with no specific method consistently outperforming others.
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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.