Should we synthesize more than we need: impact of synthetic data generation for high-dimensional cross-sectional medical data.

ABSTRACT:

Objective

In medical research and education, generative artificial intelligence/machine learning (AI/ML) models to synthesize artificial medical data can enable the sharing of high-quality data while preserving the privacy of patients. Given that such data is often high-dimensional, a relevant consideration is whether to synthesize the entire dataset when only a task-relevant subset is needed. This study evaluates how the number of variables in training impacts fidelity, utility, and privacy of the synthetic data (SD).

Material and methods

We used 12 cross-sectional medical datasets, defined a downstream task with corresponding core variables, and derived 6354 variants by adding adjunct variables to the core. SD was generated using 7 different generative models and evaluated

SUBMITTER: Pilgram L 

PROVIDER: S-EPMC12646385 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature