Large Language Models are Powerful Electronic Health Record Encoders (2502.17403v3)

Abstract: Electronic Health Records (EHRs) offer considerable potential for clinical prediction, but their complexity and heterogeneity present significant challenges for traditional machine learning methods. Recently, domain-specific EHR foundation models trained on large volumes of unlabeled EHR data have shown improved predictive accuracy and generalization. However, their development is constrained by limited access to diverse, high-quality datasets, and by inconsistencies in coding standards and clinical practices. In this study, we explore the use of general-purpose LLMs to encode EHR into high-dimensional representations for downstream clinical prediction tasks. We convert structured EHR data into markdown-formatted plain text documents by replacing medical codes with natural language descriptions. This enables the use of LLMs and their extensive semantic understanding and generalization capabilities as effective encoders of EHRs without requiring access to private medical training data. We show that LLM-based embeddings can often match or even surpass the performance of a specialized EHR foundation model, CLMBR-T-Base, across 15 diverse clinical tasks from the EHRSHOT benchmark. To demonstrate generalizability, we further evaluate the approach on the UK Biobank (UKB) cohort, a population distinct from that used to train CLMBR-T-Base. Notably, one of the tested LLM-based models achieves superior performance for disease onset, hospitalization, and mortality prediction, highlighting robustness to shifts in patient populations. Our findings suggest that repurposed general-purpose LLMs for EHR encoding provide a scalable and generalizable alternative to domain-specific models for clinical prediction.