
A novel tabular EHR generation method, EHR-D3PM, enables both unconditional and conditional generation of realistic synthetic EHR data using a discrete diffusion model, significantly outperforming existing generative baselines on fidelity, utility and privacy metrics.


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guided-discrete-diffusion-for-generating-realistic-synthetic-electronic-health-records


Guided Discrete Diffusion for Generating Realistic Synthetic Electronic Health Records


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The authors present the CEHR-GPT framework for generating synthetic electronic health records (EHRs) that preserve the temporal dependencies and chronological patient timelines, enabling applications such as disease progression analysis, population estimation, and counterfactual reasoning.


generating-synthetic-electronic-health-records-with-chronological-patient-timelines


Generating Synthetic Electronic Health Records with Chronological Patient Timelines
