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An Evaluation of Pretrained Generative Models for Augmenting Small Health Data: Comparative Modeling Study.

Margerie Huet-Dastarac^1,2, Fida K Dankar², Dan Liu^1,2

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

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This summary is machine-generated.

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.

Keywords:

binary classification data augmentation machine learning small data regime synthetic data generation tabular data

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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.