Synthetic Data Generation for Emergency Medical Systems: A Systematic Comparison of Tabular GAN Extensions

Md Kabir, Md Majharul Islam Nayem, Sven Tomforde

2025

Abstract

The generation of synthetic medical data has gained significant attention due to privacy concerns and the limited availability of real medical datasets. Various methods and techniques have been employed across domains to address these challenges, especially for tabular data. This study presents a comparative analysis of multiple generative models and privacy concerns. In addition, we propose the WLSTM-GAN model, which is evaluated with and without privacy constraints specifically for three medical tabular datasets. Our model is designed to handle both categorical and continuous features independently, incorporating a single generator with two specialized LSTM networks, as well as two distinct discriminators tailored for continuous and categorical data. We demonstrate that LSTM-based architectures can be effectively adapted for tabular data generation, with our WLSTM-GAN outperforming several existing models in fidelity and privacy preservation.

Paper Citation

in Harvard Style

Kabir M., Nayem M. and Tomforde S. (2025). Synthetic Data Generation for Emergency Medical Systems: A Systematic Comparison of Tabular GAN Extensions. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 1199-1206. DOI: 10.5220/0013307200003890

in Bibtex Style

@conference{icaart25,
author={Md Kabir and Md Nayem and Sven Tomforde},
title={Synthetic Data Generation for Emergency Medical Systems: A Systematic Comparison of Tabular GAN Extensions},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={1199-1206},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013307200003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Synthetic Data Generation for Emergency Medical Systems: A Systematic Comparison of Tabular GAN Extensions
SN - 978-989-758-737-5
AU - Kabir M.
AU - Nayem M.
AU - Tomforde S.
PY - 2025
SP - 1199
EP - 1206
DO - 10.5220/0013307200003890
PB - SciTePress