Generating Multi-Table Time Series EHR from Latent Space with Minimal Preprocessing
Journal:
arXiv
Published Date:
Jul 9, 2025
Abstract
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 or structured codes only), we
introduce RawMed, the first framework to synthesize multi-table, time-series
EHR data that closely resembles raw EHRs. Using text-based representation and
compression techniques, RawMed captures complex structures and temporal
dynamics with minimal preprocessing. We also propose a new evaluation framework
for multi-table time-series synthetic EHRs, assessing distributional
similarity, inter-table relationships, temporal dynamics, and privacy.
Validated on two open-source EHR datasets, RawMed outperforms baseline models
in fidelity and utility. The code is available at
https://github.com/eunbyeol-cho/RawMed.
