Synthetic EHR

Synthetic electronic health record (EHR) generation aims to create realistic, privacy-preserving artificial EHR data for research and development purposes, overcoming limitations imposed by real data's sensitive nature. Current research heavily utilizes diffusion models and generative adversarial networks (GANs), focusing on improving data fidelity, mitigating privacy risks, and handling the complexities of temporal data and multi-label diagnoses. This field is crucial for advancing healthcare research and applications by enabling broader data access while protecting patient confidentiality, facilitating the development and evaluation of machine learning models in clinical settings.