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An Evaluation of Pretrained Generative Models for Augmenting Small Health Data: Comparative Modeling Study.
Margerie Huet-Dastarac1,2, Fida K Dankar2, Dan Liu1,2
1School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, 451 Smyth Rd, Ottawa, ON, K1H 8M5, Canada, 1 613-562-5800.
Journal of Medical Internet Research
|June 15, 2026
Summary
Synthetic data generation (SDG) can improve healthcare machine learning but simple sampling with replacement is most effective for small datasets. Augmenting Tabular Prior-Data Fitted Network (TabPFN) with this method offers comparable performance to complex SDG techniques with fewer computational demands.
Area of Science:
- Healthcare Machine Learning
- Data Augmentation Techniques
- Computational Efficiency in AI
Background:
- Healthcare data scarcity poses challenges for machine learning model development due to privacy concerns and high acquisition costs.
- Small datasets (median ~600 records) in healthcare hinder model generalization, increasing risks of overfitting and bias.
- Synthetic Data Generation (SDG) offers a privacy-preserving method to create artificial patient data, enabling robust model training.
Purpose of the Study:
- To comprehensively assess Synthetic Data Generation (SDG)-augmented training for outcome prediction across diverse machine learning models.
- To evaluate the effectiveness of pretrained versus non-pretrained SDG models on small healthcare datasets (50 and 350 records).
- To compare the predictive performance of three state-of-the-art classifiers (gradient boosting, LLMs, TabPFN) on small datasets.
Main Methods:
- Compared three classifiers: light gradient boosting, large language models (LLMs) for tabular data, and Tabular Prior-Data Fitted Network (TabPFN).
- Augmented classifiers using state-of-the-art SDG methods (Bayesian networks, GANs, VAEs, sequential trees) and LLMs.
- Evaluated performance on 13 healthcare datasets, focusing on binary classification tasks with small training set sizes.
Main Results:
- Augmented Tabular Prior-Data Fitted Network (TabPFN) demonstrated superior predictive performance (AUC, integrated calibration index).
- Both SDG and LLM models showed overfitting tendencies on the examined small dataset sizes.
- Simple data augmentation via sampling with replacement yielded performance comparable to complex SDG and LLM methods for TabPFN.
Conclusions:
- Recommends augmenting Tabular Prior-Data Fitted Network (TabPFN) with sampling with replacement for small-data binary classification tasks.
- This simple augmentation strategy achieves performance comparable to complex SDG techniques.
- Offers substantial computational advantages over advanced Synthetic Data Generation methods.
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