Ulf Näslund

Evaluation of synthetic data generation for longitudinal 6-year follow-up subclinical cardiovascular disease data using GAN models

This study evaluates CTGAN and TGAN for generating synthetic longitudinal CVD data, finding CTGAN offers better fidelity than TGAN but has lower performance in temporal fidelity and for younger age groups.

Evaluating in-context learning with prompting regimes for transformer-based synthetic health tabular data generation

This study evaluates zero shot, one shot, and few shot prompting regimes for transformer-based synthetic health tabular data generation using small models, finding zero shot improves fidelity but reduces privacy.