CTGAN-driven synthetic data generation: A multidisciplinary, expert-guided approach (TIMA)

Abstract

Objective

We generated synthetic data starting from a population of two hundred thirty-eight adults SARS-CoV-2 positive patients admitted to the University Hospital of Brussels, Belgium, in 2020, utilizing a Conditional Tabular Generative Adversarial Network (CTGAN)-based technique with the aim of testing the performance, representativeness, realism, novelty, and diversity of synthetic data generated from a small patient sample. A Multidisciplinary Approach (TIMA) incorporates active participation from a medical team throughout the various stages of this process.

Methods

The TIMA committee scrutinized data for inconsistencies, implementing stringent rules for variables unlearned by the system. A sensitivity analysis determined 100,000 epochs, leading to the generation of 10,000 synthetic data. The model's performance was tested using a general-purpose dataset, comparing real and synthetic data.

Results

Outcomes indicate the robustness of our model, with an average contingency score of 0.94 across variable pairs in synthetic and real data. Continuous variables exhibited a median correlation similarity score of 0.97. Novelty received a top score of 1. Principal Component Analysis (PCA) on synthetic values demonstrated diversity, as no patient pair displayed a zero or close-to-zero value distance. Remarkably, the TIMA committee's evaluation revealed that synthetic data was recognized as authentic by nearly 100%.

Conclusions

Our trained model exhibited commendable performance, yielding high representativeness in the synthetic dataset compared to the original. The synthetic dataset proved realistic, boasting elevated levels of novelty and diversity.