arXiv AI October 21, 2026 By Tong Ding, Caiwei Tian, Dongmin Bang, Ming Y. Lu, Sophia J. Wagner, Andrew Zhang, Rowland Pettit, Long Phi Le, Faisal Mahmood

Discrete Neural Interlingua for Ontology-Agnostic EHR Modeling

Read the original on arXiv AI →

The paper introduces MEDIATOR, a discrete, ontology‑agnostic tokenizer that maps any clinical concept—whether encoded, synonymous, or free‑text—into a shared vector‑quantized codebook. By collapsing equivalent expressions and organizing distinct concepts in a medically meaningful space, MEDIATOR serves as the token interface for an EHR Transformer, boosting mean AUPRC by 4.0 points on 20 MIMIC‑III tasks and 8.6 points in zero‑shot transfer to 14 eICU tasks, while enabling full event coverage and transferable attribution across hospitals.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 24

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

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By Lin Lawrence Guo, Adam Paul Yan, Emily Vettese, Lillian Sung

arXiv AI

Oct 15

SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation

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llms rag

arXiv Machine Learning

Sep 15

Knowledge-Enriched Structured EHR Features for 30-Day Hospital Readmission Prediction on MIMIC-IV

arXiv:2609.15713v1 Announce Type: new Abstract: Recent approaches to 30-day hospital readmission prediction rely on pre-trained language models applied to discharge summaries. Although these methods...

By Mohamad Najafi, Hongyun Fu, Mathias Brochhausen, Jian Wu, Yaohang Li

llms rag safety

arXiv AI

Jun 16

Hierarchical Modeling of ICD Codes in EHR Foundation Models

arXiv:2606. 15447v1 Announce Type: new Abstract: Electronic health record foundation models typically treat ICD diagnosis codes as flat tokens, overlooking the clinically meaningful hierarchical structure that captures disease families, subcategories, and fine-grained diagnostic detail.

By Megha Thukral, Dong Gyun Kang, Rudra Pratap Singh, Shruthi Kashinath Hiremath, Katrin H\"ansel, Thomas Pl\"otz

llms rag safety

arXiv AI

Jun 30

Primary ICD Category Prediction using LLM-based Probing

arXiv:2606. 28798v1 Announce Type: new Abstract: Objective: ICD codes are central to reimbursement, research, and population health surveillance, yet automated coding systems often struggle to integrate diagnostic signals from both clinical narratives and structured electronic health record (EHR) variables.

By Chengyuan Liu, Xinyue Zhang, Yao Li, Guanting Chen

llms rag multimodal

arXiv Machine Learning

Sep 14

Representation Before Training: A Practical Benchmark for Generative Medical Event Model Tokenization

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By Inhyeok Lee, Luke Solo, Michael C. Burkhart, Bashar Ramadan, Sahil Sethi, Sarah Jabbour, William F. Parker, Brett K. Beaulieu-Jones

llms rag nlp efficiency benchmarks

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Related stories

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

SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation

Knowledge-Enriched Structured EHR Features for 30-Day Hospital Readmission Prediction on MIMIC-IV

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Representation Before Training: A Practical Benchmark for Generative Medical Event Model Tokenization