TabGraphSyn: Graph-Guided Latent Diffusion for High-Fidelity and Privacy-Conscious Clinical Data Generation

2025-12-29 health informatics Title + abstract only

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The critical need for accessible patient data in clinical research is often hindered by privacy regulations and data scarcity. While synthetic data generation offers a promising solution, existing generative models face key limitations. GANs can suffer from training instability, while diffusion models typically process records independently and often neglect the local neighborhood structure of the data manifold. To address this gap, we introduce TabGraphSyn, a two-stage generative framework for ...

TabGraphSyn: Graph-Guided Latent Diffusion for High-Fidelity and Privacy-Conscious Clinical Data Generation

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