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Related Experiment Videos

Quantifying Fidelity and Utility in Synthetic Healthcare Data.

Sina Sadeghi^1,2, John Gamisch^1,2, Toralf Kirsten^1,2,3

¹Department for Medical Data Science, Leipzig University Medical Center, Leipzig, Germany.

Studies in Health Technology and Informatics

|May 23, 2026

Summary

This summary is machine-generated.

Generative models create synthetic data (SD) that mirrors real data (RD) for healthcare AI. This study shows optimized models produce high-fidelity SD with comparable predictive utility to RD, overcoming data scarcity and privacy barriers.

Keywords:

data fidelity data utility healthcare machine learning synthetic data

Related Experiment Videos

Area of Science:

Healthcare Machine Learning
Data Privacy
Artificial Intelligence

Background:

High-quality clinical datasets are scarce, hindering predictive model development in healthcare.
Strict privacy regulations pose significant challenges for accessing and utilizing sensitive patient data.
Generative models offer a potential solution by creating synthetic data (SD) that preserves real data (RD) characteristics while protecting privacy.

Purpose of the Study:

To introduce a structured framework for evaluating the fidelity and utility of synthetic data (SD) generated by tabular models.
To assess the performance of three generative models: Conditional Tabular GAN (CTGAN), CopulaGAN, and Tabular Variational Autoencoder (TVAE).
To determine the effectiveness of Pairwise Correlation Distance (PCD) and Wasserstein Distance (WSD) as metrics for SD quality.

Main Methods:

Utilized the Pima Indians Diabetes Dataset to generate synthetic data (SD) using CTGAN, CopulaGAN, and TVAE.
Quantified synthetic data fidelity using Pairwise Correlation Distance (PCD) and Wasserstein Distance (WSD).
Evaluated synthetic data utility by measuring binary classification performance and comparing it to models trained on real data (RD).

Main Results:

Optimized generative models successfully produced high-fidelity synthetic data (SD).
The predictive accuracy of models trained on SD was comparable to those trained on real data (RD).
Pairwise Correlation Distance (PCD) and Wasserstein Distance (WSD) proved to be reliable indicators of SD quality.

Conclusions:

Generative models can effectively create high-fidelity synthetic data (SD) for healthcare machine learning applications.
Optimized generative models mitigate privacy risks associated with real clinical data (RD) while maintaining predictive utility.
Pairwise Correlation Distance (PCD) and Wasserstein Distance (WSD) are valuable metrics for optimizing generative models in healthcare.