| Title: | Large language models are powerful electronic health record encoders |
| Journal: | npj Digital Medicine |
| Published: | 6 Jul 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42410244/ |
| DOI: | https://doi.org/10.1038/s41746-026-02915-9 |
| URL: | https://www.nature.com/articles/s41746-026-02915-9_reference.pdf |
Publication 20422
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Abstract
Electronic health records (EHRs) offer considerable potential for clinical prediction, but their complexity and heterogeneity challenge traditional machine learning. Domain-specific electronic health record foundation models trained on unlabeled EHR data have shown improved predictive accuracy and generalization. However, their development is constrained by limited data access and site-specific vocabularies. We convert EHR data into plain text by replacing medical codes with natural-language descriptions, enabling general-purpose large language models (LLMs) to produce high-dimensional embeddings for downstream prediction tasks without access to private medical training data. LLM-based embeddings perform on par with a specialized EHR foundation model, CLMBR-T-Base, across 15 clinical tasks from the EHRSHOT benchmark. In an external validation using the UK Biobank, an LLM-based model shows statistically significant improvements for some tasks, which we attribute to higher vocabulary coverage and slightly better generalization. Overall, we reveal a trade-off between the computational efficiency of specialized EHR models and the portability and data independence of LLM-based embeddings.</p>
8 Authors
- Stefan Hegselmann
- Georg von Arnim
- Tillmann Rheude
- Noel Kronenberg
- David Sontag
- Gerhard Hindricks
- Roland Eils
- Benjamin Wild
1 Application
| Application ID | Title |
|---|---|
| 49966 | Learning Intergenic Interactions Shaping Human Genotype-Phenotype Relations |
Enabling scientific discoveries that improve human health