Generating Multi-Table Time Series EHR from Latent Space with Minimal Preprocessing

Electronic Health Records (EHR) are time-series relational databases that record patient interactions and medical events over time, serving as a critical resource for healthcare research and applications. However, privacy concerns and regulatory restrictions limit the sharing and utilization of such sensitive data, necessitating the generation of synthetic EHR datasets. Unlike previous EHR synthesis methods--which typically generate medical records consisting of expert-chosen features (e.g., a few vital signs, structured codes only)--we introduce RawMed, the first framework to synthesize multi-table, time-series EHR data that closely resembles raw EHRs.