TempEHR: A Temporal Dependency-Based Approach for Synthesizing Electronic Health Records

Abstract

Synthetic Electronic Health Records (EHRs) provide a viable means of accessing EHR data while addressing the privacy concerns related to the use of EHRs. A key characteristic of EHRs is the irregular timing of clinical events, admissions, and associated temporal trends. Many existing models for generating synthetic EHRs overlook these temporal irregularities, often assuming uniform intervals between clinical events for each patient and neglecting the time component, which hinders the representation of true temporal dynamics. To address these limitations, we propose TempEHR, a framework designed to synthesise EHRs, emphasising temporal awareness. We employ a time-aware Variational Autoencoder (VAE), specifically a Maximum Mean Discrepancy VAE (MMD-VAE), leveraging Time-aware Long Short-Term Memory (T-LSTM) layers to generate temporal synthetic EHRs along with time information. Simultaneously, we enhance the temporal awareness of our proposed model with a novel network we refer to as a TrendFinder. TrendFinder leverages a moving average to extract the temporal patterns inherent in irregular longitudinal EHR data. This approach seeks to enhance the fidelity and usefulness of synthetic EHRs for research and clinical applications. We assess the effectiveness of TempEHR using EHRs from the Medical Information Mart for Intensive Care (MIMIC-IV) repository. Our results demonstrate the potential of the proposed method in capturing the temporal patterns present in EHRs in utility, fidelity and privacy evaluations.