aceLarge language models are powerful electronic health record encoders
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, enabli…
General-purpose LLMs can encode EHRs as plain-text descriptions to produce embeddings for clinical prediction without private medical training data, matching a specialized EHR foundation model across 15 tasks and improving on some tasks in UK Biobank external validation.
LLM-based embeddings trade portability and data independence for lower computational efficiency compared to specialized EHR models.
Evidence
- Peer-reviewednpj Digital Medicine2026-07-06
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Truvace Impact Record TRV-2026-0249, v1: “Large language models are powerful electronic health record encoders.” Truvace, 2026-07-17. /record/TRV-2026-0249 (accessed at citation time). sha256 4c4518f7d47d6127…
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