Large Language Models are Powerful Electronic Health Record Encoders
Journal:
arXiv
Published Date:
Feb 24, 2025
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 Large Language Models (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.
