Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models
This work addresses data scarcity and quality issues in healthcare for researchers and practitioners, but it is incremental as it builds on existing generative techniques with specific enhancements.
The paper tackled the problem of generating synthetic electronic health records (EHR) by addressing limitations in existing methods, such as poor temporal dependency modeling and lack of time information generation, and proposed a diffusion-based model called EHRPD that improved generation quality and diversity, achieving efficacy in fidelity, privacy, and utility on two public datasets.
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 (PDDPM). 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.