Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models | JoVE Visualize

Area of Science:

Health Informatics
Artificial Intelligence in Healthcare
Biomedical Data Science

Background:

Synthesizing electronic health records (EHR) is crucial for addressing data scarcity, enhancing data quality, and ensuring fairness in healthcare applications.
Current generative models like GANs and VAEs often fail to adequately capture temporal dependencies and time information in EHR data, limiting generation quality.
Existing methods struggle with effective visit representation learning due to simplistic mapping functions, compromising the fidelity of synthetic EHR data.

Purpose of the Study:

To propose EHRPD, a novel diffusion-based model for generating synthetic electronic health records (EHR) that accurately models temporal dynamics and time intervals.
To enhance the quality and diversity of synthetic EHR data by introducing a time-aware visit embedding module and a predictive denoising diffusion probabilistic model (P-DDPM).
To optimize the P-DDPM model using a predictive U-Net (PU-Net) for improved EHR data synthesis.

Main Methods:

Developed EHRPD, a diffusion-based model predicting the next patient visit and estimating time intervals.
Introduced a time-aware visit embedding module to improve representation learning.
Implemented a predictive denoising diffusion probabilistic model (P-DDPM) optimized by a predictive U-Net (PU-Net).

Main Results:

EHRPD demonstrated superior performance in generating synthetic EHR data across fidelity, privacy, and utility metrics on two public datasets.
The proposed model effectively addresses limitations of existing methods by accurately modeling temporal dependencies and time information.
Experimental results confirm the efficacy of the time-aware embedding and PU-Net in enhancing generation quality and diversity.

Conclusions:

EHRPD offers a significant advancement in synthetic EHR data generation, overcoming key limitations of current generative techniques.
The model's ability to capture temporal dynamics and time intervals enhances the utility and reliability of synthetic EHR data.
EHRPD provides a promising solution for data scarcity and fairness challenges in healthcare through high-quality synthetic data generation.