MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model

• URL: http://arxiv.org/abs/2302.04355v1
• Date: Wed, 8 Feb 2023 22:06:34 GMT
• Title: MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model
• Authors: Huan He, Shifan Zhao, Yuanzhe Xi, Joyce C Ho
• Abstract summary: 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.
• Score: 5.677138915301383
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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