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

Abstract excerpt

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. T...

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• Benchmarking
• Electronic Health Records
• Phenotype
• Software

Identifiers and source

Literature Corpus work

d9296644-e4b9-52f6-80d7-f96ea36c39b1

DOI

10.64898/2025.12.28.25342851