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

Advanced Data Analytics in eHealth (196) Data Science (347) Machine Learning (1973) Big Data (305) Clinical Information and Decision Making (1809)

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 (10) 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
  3. Santangelo G, Nicora G, Bellazzi R, Dagliati A. How good is your synthetic data? SynthRO, a dashboard to evaluate and benchmark synthetic tabular data. BMC Medical Informatics and Decision Making 2025;25(1) View
  4. Kang H, Ko M, Ryu K. Tabular transformer generative adversarial network for heterogeneous distribution in healthcare. Scientific Reports 2025;15(1) View
  5. Pasculli G, Virgolin M, Myles P, Vidovszky A, Fisher C, Biasin E, Mourby M, Pappalardo F, D'Amico S, Torchia M, Chebykin A, Carbone V, Emili L, Roeshammar D. Synthetic Data in Healthcare and Drug Development: Definitions, Regulatory Frameworks, Issues. CPT: Pharmacometrics & Systems Pharmacology 2025;14(5):840 View
  6. Ahmed H, Nepomuceno J, Vega-Márquez B, Nepomuceno-Chamorro I. Synthetic Data Generation for Healthcare: Exploring Generative Adversarial Networks Variants for Medical Tabular Data. International Journal of Data Science and Analytics 2025 View
  7. Paladugu P, Kumar R, Yelamanchi J, Waisberg E, Ong J, Masalkhi M, Gowda C, Lee R, Amiri D, Jagadeesan R, Zaman N, Tavakkoli A, Lee A. Prediction of Cerebrospinal Fluid (CSF) Pressure with Generative Adversarial Network Synthetic Plasma-CSF Biomarker Pairing. Neuroinformatics 2025;23(3) View
  8. Boni M, Gervasio Sene Junior I, Martins da Costa R. Tabular Data Augmentation Using Artificial Intelligence: A Systematic Review and Taxonomic Framework. IEEE Access 2025;13:138950 View

Books/Policy Documents

  1. Ray P. Generative Artificial Intelligence for Biomedical and Smart Health Informatics. View

Conference Proceedings

  1. Neravetla A, Samreen S, Mohammed A, Jiwani N, Logeshwaran J. 2024 International Conference on Integrated Circuits and Communication Systems (ICICACS). An Improved Lung Cancer Prediction Algorithm using Generative Adversarial Network in Modern Healthcare View