arXiv AI

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.

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

Aug 4

xMICD: Explainable Representation of Multiple ICD Codes

arXiv:2608. 00935v1 Announce Type: new Abstract: Electronic Health Records (EHRs) are widely used for clinical risk prediction using machine learning.

By Pat Vatiwutipong, Kumkup Keeratisiwakul, Albert Phuoc Kien Van Truong, Nutcha Yodrabum, Wasin Pansiritanachot, Marvin N. Wright, Thanapon Noraset

rag safety

arXiv Machine Learning

Jul 20

LLM4EHR: Aligning Clinical Time Series with Medical Event Sequences via Large Language Models

arXiv:2607. 15447v1 Announce Type: new Abstract: Recent research in clinical machine learning, focusing on outcome predictions in intensive care unit (ICU), has shifted from bespoke supervised models to foundation models, utilising modern representation learning methods.

By Jingteng Li, Alexander Capstick, Louise Rigny, Iona Biggart, Neil J Sebire, Payam Barnaghi

llms rag benchmarks

arXiv Machine Learning

Aug 10

MiGHT-EHR: A Multi-task Graph Transformer for Heterogeneous Temporal Electronic Health Records

arXiv:2608. 06430v1 Announce Type: new Abstract: Learning from Electronic Health Records (EHRs) has gained significant attention due to its potential to improve clinical prediction.

By Anirudh Rayas, Yuan Wang, Pavan Turaga

llms benchmarks

arXiv Machine Learning

Jun 8

Accelerating Reproducible Research in Synthetic EHR Generation

arXiv:2606. 06990v1 Announce Type: new Abstract: The generation of high-fidelity synthetic Electronic Health Records (EHR) is crucial for advancing medical research while preserving patient privacy.

By Jalen Jiang, Chufan Gao, Ethan Rasmussen, Stephen Z. Xie, Jimeng Sun

llms diffusion benchmarks safety

arXiv AI

Jun 3

ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning

arXiv:2606. 02802v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong natural-language reasoning abilities for clinical decision support, but struggle to effectively model structured longitudinal electronic health records (EHRs).

By Bo-Hong Wang, Baicheng Peng, Ruilin Wang, Jun Bai, Ziyang Song, Yue Li

llms multimodal benchmarks safety

arXiv Machine Learning

Jun 8

One Loss to Rule Them All: Marked Time-to-Event for Structured EHR Foundation Models

arXiv:2602. 00541v2 Announce Type: replace Abstract: Clinical events captured in Electronic Health Records (EHR) are irregularly sampled and may consist of a mixture of discrete events and numerical measurements, such as laboratory values or treatment dosages.

By Zilin Jing, Vincent Jeanselme, Yuta Kobayashi, Simon A. Lee, Chao Pang, Aparajita Kashyap, Yanwei Li, Xinzhuo Jiang, Shalmali Joshi

arXiv Machine Learning

Jun 24

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

arXiv:2606. 24102v1 Announce Type: cross 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.

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

arXiv AI

Jul 15

Graph-Constrained Policy Learning for Extreme Clinical Code Prediction

arXiv:2607. 11954v1 Announce Type: cross Abstract: Clinical code prediction maps unstructured discharge summaries to ICD-10-CM leaf codes in a large, sparse, and deeply hierarchical label space.

By Amritpal Singh, Sebastian Torres, Khawar Shakeel, Syed Ahmad Chan Bukhari

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 7

OC-Distill: Ontology-aware Contrastive Learning with Cross-Modal Distillation for ICU Risk Prediction

arXiv:2604. 16878v2 Announce Type: replace Abstract: Early prediction of severe clinical deterioration and remaining length of stay can enable timely intervention and better resource allocation in high-acuity settings such as the ICU.

By Zhongyuan Liang, Junhyung Jo, Hyang-Jung Lee, Sang Kyu Kim, Irene Y. Chen

fine-tuning efficiency multimodal benchmarks

arXiv AI

Jun 2

Retrieval-aligned Tabular Foundation Models Enable Robust Clinical Risk Prediction in Electronic Health Records Under Real-world Constraints

arXiv:2604. 01841v2 Announce Type: replace Abstract: Clinical prediction from structured electronic health records (EHRs) is challenging due to high dimensionality, heterogeneity, class imbalance, and distribution shift.

By Minh-Khoi Pham, Thang-Long Nguyen Ho, Thao Thi Phuong Dao, Tai Tan Mai, Minh-Triet Tran, Marie E. Ward, Una Geary, Rob Brennan, Nick McDonald, Martin Crane, Marija Bezbradica

llms rag benchmarks safety

arXiv AI

Jul 8

X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Models

arXiv:2607. 06163v1 Announce Type: cross Abstract: Foundation Models for Electronic Health Records (FEMRs) are pretrained on large-scale structured patient data, enabling them to convert longitudinal patient trajectories into generalizable representations for diverse clinical prediction tasks.

By Jie Huang, Pengfei Yin, Zihan Xu, Daniel Capurro, Mike Conway, Ting Dang

llms safety

Related stories