Published on in Vol 11 (2023)
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/47859, first published
.
![Synthetic Tabular Data Based on Generative Adversarial Networks in Health Care: Generation and Validation Using the Divide-and-Conquer Strategy Synthetic Tabular Data Based on Generative Adversarial Networks in Health Care: Generation and Validation Using the Divide-and-Conquer Strategy](https://asset.jmir.pub/assets/1ccf96192af36b19a27fb307dd445890.png 480w,https://asset.jmir.pub/assets/1ccf96192af36b19a27fb307dd445890.png 960w,https://asset.jmir.pub/assets/1ccf96192af36b19a27fb307dd445890.png 1920w,https://asset.jmir.pub/assets/1ccf96192af36b19a27fb307dd445890.png 2500w)
Journals
- El Emam K, Leung T, Malin B, Klement W, Eysenbach G. Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS). Journal of Medical Internet Research 2024;26:e52508 View
- Papadaki E, Vrahatis A, Kotsiantis S. Exploring Innovative Approaches to Synthetic Tabular Data Generation. Electronics 2024;13(10):1965 View