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PORTER: Language-Grounded Event Representations for Portable Structured EHR Foundation Models

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arXiv:2606. 24102v1 Announce Type: new Abstract: Most electronic health record (EHR) foundation models encode clinical events as discrete event tokens from a fixed vocabulary and therefore cannot directly represent events containing unseen concepts or new combinations of concepts and attributes such as numeric values.

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Authors: Lin Lawrence Guo, Adam Paul Yan, Emily Vettese, Lillian Sung
Released: 2026-06-24 (15d ago)
Topic: Related to current project
Relation to my work: Read next because PORTER: Language-Grounded Event Representations for Portable Structured EHR Foundation Models overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, text, rect, token, line, rate, without, trained. Source: arxiv cs.CL (NLP).
arXiv: 2606.24102

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