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

Background: Synthetic data generation (SDG) has emerged as a promising solution to address data scarcity in health care, where privacy concerns, regulatory barriers, and the high cost of data acquisition limit access to real patient datasets. Machine learning models in this domain often operate in low-data regimes, with training set sizes as low as 20 and a median dataset size of around 600 records-conditions that hinder model generalization and increase the risks of overfitting and bias. SDG addresses these challenges by producing artificial samples that mimic real-world patient data, enabling robust and privacy-preserving model development. Objective: This study was a comprehensive assessment of SDG-augmented training across a wide array of models-both pretrained and non-pretrained-for outcome prediction in 13 health care datasets. For small datasets of sizes 50 and 350 records, we answer 3 key questions: (1) Do pretrained SDG models generate more effective augmentations than their non-pretrained counterparts for small datasets? (2) Is augmentation beneficial for both pretrained and non-pretrained classifiers for small datasets? (3) Among 3 state-of-the-art classification models, which offers the best predictive performance on small datasets? The workload that this study aimed to improve was binary classification. Methods: The 3 classifiers considered were light gradient boosting trees, large language models (LLMs) adapted to tabular data, and Tabular Prior-Data Fitted Network (TabPFN), a transformer-based method that has become the new state of the art in terms of tabular data classification. Each classifier was augmented through different SDG methods: current state-of-the-art techniques (Bayesian networks, conditional tabular generative adversarial networks, tabular variational autoencoders, and sequential trees) and the use of LLMs for tabular data generation. Results: Augmented TabPFN demonstrated superior performance, yielding significantly higher area under the curve and integrated calibration index scores compared to other classifiers. Post hoc analysis revealed that, for the dataset sizes examined, SDG and LLM models exhibited overfitting tendencies. Notably, simple dataset augmentation through sampling with replacement achieved performance comparable to that of SDG-based and LLM-based augmentation methods for TabPFN, suggesting that gains were primarily driven by increased sample size rather than SDG. Conclusions: Given its strong performance and minimal computational overhead, we recommend augmenting TabPFN through sampling with replacement as the optimal approach for small-data binary classification tasks. This method achieves performance comparable to that of more complex SDG techniques while offering substantial computational advantages.