Large Language Models are Powerful Electronic Health Record Encoders

[2502.17403] Large Language Models are Powerful Electronic Health Record Encoders Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2502.17403 (cs) [Submitted on 24 Feb 2025 ( v1 ), last revised 7 Aug 2026 (this version, v6)] Title: Large Language Models are Powerful Electronic Health Record Encoders Authors: Stefan Hegselmann , Georg von Arnim , Tillmann Rheude , Noel Kronenberg , David Sontag , Gerhard Hindricks , Roland Eils , Benjamin Wild View a PDF of the paper titled Large Language Models are Powerful Electronic Health Record Encoders, by Stefan Hegselmann and 7 other authors View PDF HTML (experimental) Abstract: Electronic Health Records (EHRs) offer considerable potential for clinical prediction, but their complexity and heterogeneity challenge traditional machine learning. Domain-specific EHR 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. Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2502.17403 [cs.LG] (or arXiv:2502.17403v6 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2502.17403 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Stefan Hegselmann [ view email ] [v1] Mon, 24 Feb 2025 18:30:36 UTC (648 KB) [v2] Tue, 4 Mar 2025 16:36:52 UTC (648 KB) [v3] Wed, 21 May 2025 12:31:35 UTC (785 KB) [v4] Sun, 19 Oct 2025 15:10:37 UTC (1,077 KB) [v5] Mon, 13 Apr 2026 19:19:47 UTC (1,211 KB) [v6] Fri, 7 Aug 2026 22:56:19 UTC (1,190 KB) Full-text links: Access Paper: View a PDF of the paper titled Large Language Models are Powerful Electronic Health Record Encoders, by Stefan Hegselmann and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2025-02 Change to browse by: cs cs.AI cs.CL References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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