Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models
Research Poster Engineering 2025 Graduate ExhibitionPresentation by Yuan Zhong
Exhibition Number 105
Abstract
Synthesizing electronic health records (EHR) data has become a preferred strategy to address data scarcity, improve data quality, and model fairness in healthcare. However, existing approaches for EHR data generation predominantly rely on state-of-the-art generative techniques like generative adversarial networks, variational autoencoders, and language models. These methods typically replicate input visits, resulting in inadequate modeling of temporal dependencies between visits and overlooking the generation of time information, a crucial element in EHR data. Moreover, their ability to learn visit representations is limited due to simple linear mapping functions, thus compromising generation quality. To address these limitations, we propose a novel EHR data generation model called EHRPD. It is a diffusion-based model designed to predict the next visit based on the current one while also incorporating time interval estimation. To enhance generation quality and diversity, we introduce a novel time-aware visit embedding module and a pioneering predictive denoising diffusion probabilistic model (P-DDPM). Additionally, we devise a predictive U-Net (PU-Net) to optimize P-DDPM. We conduct experiments on two public datasets and evaluate EHRPD from fidelity, privacy, and utility perspectives. The experimental results demonstrate the efficacy and utility of the proposed EHRPD in addressing the aforementioned limitations and advancing EHR data generation.
Importance
Generating realistic Electronic Health Records (EHR) data is essential for advancing healthcare AI while preserving patient privacy. Existing methods struggle to model visit sequences and time intervals, limiting their usefulness. Our study introduces EHRPD, a diffusion-based model that improves EHR generation by capturing temporal dependencies and predicting visit intervals. This advancement enhances data fidelity, utility, and privacy, supporting medical research and AI development in healthcare.