Foundation models for electronic health records: representation dynamics and transferability
Journal:
arXiv
Published Date:
Apr 14, 2025
Abstract
Foundation models (FMs) trained on electronic health records (EHRs) have
shown strong performance on a range of clinical prediction tasks. However,
adapting these models to local health systems remains challenging due to
limited data availability and resource constraints. In this study, we
investigated what these models learn and evaluated the transferability of an FM
trained on MIMIC-IV to an institutional EHR dataset at the University of
Chicago Medical Center. We assessed their ability to identify outlier patients
and examined representation-space patient trajectories in relation to future
clinical outcomes. We also evaluated the performance of supervised fine-tuned
classifiers on both source and target datasets. Our findings offer insights
into the adaptability of FMs across different healthcare systems, highlight
considerations for their effective implementation, and provide an empirical
analysis of the underlying factors that contribute to their predictive
performance.
