Reliable Generation of EHR Time Series via Diffusion Models
- URL: http://arxiv.org/abs/2310.15290v2
- Date: Tue, 21 Nov 2023 22:19:09 GMT
- Title: Reliable Generation of EHR Time Series via Diffusion Models
- Authors: Muhang Tian, Bernie Chen, Allan Guo, Shiyi Jiang, Anru R. Zhang
- Abstract summary: We introduce a new method for generating diverse and realistic synthetic EHR time series data using Denoising Diffusion Probabilistic Models (DDPM)
Our results demonstrate that our approach significantly outperforms all existing methods in terms of data utility while requiring less training effort.
- Score: 4.549831511476249
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Electronic Health Records (EHRs) are rich sources of patient-level data,
including laboratory tests, medications, and diagnoses, offering valuable
resources for medical data analysis. However, concerns about privacy often
restrict access to EHRs, hindering downstream analysis. Researchers have
explored various methods for generating privacy-preserving EHR data. In this
study, we introduce a new method for generating diverse and realistic synthetic
EHR time series data using Denoising Diffusion Probabilistic Models (DDPM). We
conducted experiments on six datasets, comparing our proposed method with eight
existing methods. Our results demonstrate that our approach significantly
outperforms all existing methods in terms of data utility while requiring less
training effort. Our approach also enhances downstream medical data analysis by
providing diverse and realistic synthetic EHR data.
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