Tabular transformer generative adversarial network for heterogeneous distribution in healthcare

Abstract

In healthcare, the most common type of data is tabular data, which holds high significance and potential in the field of medical AI. However, privacy concerns have hindered their widespread use. Despite the emergence of synthetic data as a viable solution, the generation of healthcare tabular data (HTD) is complex owing to the extensive interdependencies between the variables within each record that incorporate diverse clinical characteristics, including sensitive information. To overcome these issues, this study proposed a tabular transformer generative adversarial network (TT-GAN) to generate synthetic data that can effectively consider the relationships between variables potentially present in the HTD dataset. Transformers can consider the relationships between the columns in each record using a multi-attention mechanism. In addition, to address the potential risk of restoring sensitive data in patient information, a Transformer was employed in a generative adversarial network (GAN) architecture, to ensure an implicit-based algorithm. To consider the heterogeneous characteristics of the continuous variables in the HTD dataset, the discretization and converter methodology were applied. The experimental results confirmed the superior performance of the TT-GAN than the Conditional Tabular GAN (CTGAN) and copula GAN. Discretization and converters were proven to be effective using our proposed Transformer algorithm. However, the application of the same methodology to Transformer-based models without discretization and converters exhibited a significantly inferior performance. The CTGAN and copula GAN indicated minimal effectiveness with discretization and converter methodologies. Thus, the TT-GAN exhibited considerable potential in healthcare, demonstrating its ability to generate artificial data that closely resembled real healthcare datasets. The ability of the algorithm to handle different types of mixed variables efficiently, including polynomial, discrete, and continuous variables, demonstrated its versatility and practicality in health care research and data synthesis. © The Author(s) 2025.