MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model. (arXiv:2302.04355v1 [cs.LG])

Due to patient privacy protection concerns, machine learning research in
healthcare has been undeniably slower and limited than in other application
domains. High-quality, realistic, synthetic electronic health records (EHRs)
can be leveraged to accelerate methodological developments for research
purposes while mitigating privacy concerns associated with data sharing. The
current state-of-the-art model for synthetic EHR generation is generative
adversarial networks, which are notoriously difficult to train and can suffer
from mode collapse. Denoising Diffusion Probabilistic Models, a class of
generative models …

adversarial application art current data data sharing domains ehr electronic health records generative generative adversarial networks health healthcare high machine machine learning networks patient privacy privacy protection quality research sharing state synthetic train