MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model
This addresses privacy concerns in healthcare data sharing by providing a new method for synthetic EHR generation, though it is incremental as it adapts diffusion models to a specific domain.
The paper tackles the problem of generating synthetic electronic health records (EHRs) to accelerate healthcare research while protecting patient privacy, and it shows that their diffusion-based model outperforms existing state-of-the-art methods.
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 inspired by statistical thermodynamics, have recently been shown to generate high-quality synthetic samples in certain domains. It is unknown whether these can generalize to generation of large-scale, high-dimensional EHRs. In this paper, we present a novel generative model based on diffusion models that is the first successful application on electronic health records. Our model proposes a mechanism to perform class-conditional sampling to preserve label information. We also introduce a new sampling strategy to accelerate the inference speed. We empirically show that our model outperforms existing state-of-the-art synthetic EHR generation methods.