sEHR-CE: Language modelling of structured EHR data for efficient and generalizable patient cohort expansion

Understand

Electronic health records (EHR) offer unprecedented opportunities for in-depth clinical phenotyping and prediction of clinical outcomes.

• Combining multiple data sources is crucial to generate a complete picture of disease prevalence, incidence and trajectories.
• The standard approach to combining clinical data involves collating clinical terms across different terminology systems using curated maps, which are often inaccurate and/or incomplete.
• Here, we propose sEHR-CE, a novel framework based on transformers to enable integrated phenotyping and analyses of heterogeneous clinical datasets without relying on these mappings.