This paper is in the following e-collection/theme issue:

Advanced Data Analytics in eHealth (133) Data Science (270) Machine Learning (1465) Big Data (240) Clinical Information and Decision Making (1407)

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

Synthetic Tabular Data Based on Generative Adversarial Networks in Health Care: Generation and Validation Using the Divide-and-Conquer Strategy

Authors of this article:

Ha Ye Jin Kang1, 2 Author Orcid Image ;   Erdenebileg Batbaatar3 Author Orcid Image ;   Dong-Woo Choi3 Author Orcid Image ;   Kui Son Choi3, 4 Author Orcid Image ;   Minsam Ko1, 5 Author Orcid Image ;   Kwang Sun Ryu2, 3 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

Journals

  1. 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
  2. Papadaki E, Vrahatis A, Kotsiantis S. Exploring Innovative Approaches to Synthetic Tabular Data Generation. Electronics 2024;13(10):1965 View