Synthesize high-dimensional longitudinal electronic health records via hierarchical autoregressive language model.

ABSTRACT: Synthetic electronic health records (EHRs) that are both realistic and privacy-preserving offer alternatives to real EHRs for machine learning (ML) and statistical analysis. However, generating high-fidelity EHR data in its original, high-dimensional form poses challenges for existing methods. We propose Hierarchical Autoregressive Language mOdel (HALO) for generating longitudinal, high-dimensional EHR, which preserve the statistical properties of real EHRs and can train accurate ML models without privacy concerns. HALO generates a probability density function over medical codes, clinical visits, and patient records, allowing for generating realistic EHR data without requiring variable selection or aggregation. Extensive experiments demonstrated that HALO can generate high-fidelity data wi

SUBMITTER: Theodorou B 

PROVIDER: S-EPMC10471716 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature