arXiv AI

Temporal Sepsis Modeling: a Relational and Explainable-by-Design Framework

arXiv:2601. 21747v4 Announce Type: replace-cross Abstract: Sepsis remains one of the most complex and heterogeneous syndromes in intensive care.

arXiv Machine Learning

Aug 12

GARLIC: Graph Attention-based Relational Learning of Multivariate Time Series in Intensive Care

arXiv:2608. 10969v1 Announce Type: new Abstract: Healthcare data, such as Intensive Care Unit (ICU) records, comprise heterogeneous multivariate time series sampled at irregular intervals with pervasive missingness.

By Ruirui Wang, Yanke Li, Manuel G\"unther, Diego Paez-Granados

benchmarks

arXiv AI

Jul 1

Can Physician Expertise Improve Machine Learning Identification of Delirium?

arXiv:2606. 30651v1 Announce Type: cross Abstract: Delirium is common in hospitalized patients and is often missed in routine care.

By Xinyu Qin, Vicky Ye, Ruiheng Yu, Lu Wang

llms

arXiv AI

Jun 30

NEURON: A Neuro-symbolic System for Grounded Clinical Explainability

arXiv:2605. 01189v2 Announce Type: replace Abstract: Clinical AI adoption is hindered by the black-box/grey-box nature of high-performing models, which lack the ontological grounding and narrative transparency required for professional-level explainability.

By Anuradha Chandrasekaran, Dimitrios Zikos, Mutlu Mete, Alan Pang, Brady D. Lund, Kewei Sha

llms rag safety

arXiv AI

Jul 21

Enhancing LLMs' Clinical Reasoning with Real-World Data from a Nationwide Sepsis Registry

arXiv:2505. 02722v2 Announce Type: replace Abstract: Although large language models (LLMs) have demonstrated impressive reasoning capabilities across general domains, their effectiveness in real-world clinical practice remains limited.

By Junu Kim, Chaeeun Shim, Sungjin Park, Su Yeon Lee, Gee Young Suh, Chae-Man Lim, Seong Jin Choi, Song Mi Moon, Kyoung-Ho Song, Eu Suk Kim, Hong Bin Kim, Sejoong Kim, Chami Im, Dong-Wan Kang, Yong Soo Kim, Hee-Joon Bae, Sung Yoon Lim, Han-Gil Jeong, Edward Choi

llms reinforcement-learning fine-tuning safety

arXiv AI

Jun 2

LLMs for Cardiovascular Risk Prediction from Structured Clinical Data

arXiv:2606. 00031v1 Announce Type: cross Abstract: Coronary artery disease (CAD) remains one of the leading causes of death globally, highlighting the need for reliable predictive systems to support early diagnosis and risk assessment.

By Jeba Maliha, Md Rafiul Kabir

llms

arXiv AI

Jun 18

Language Models as Interfaces, Not Oracles: A Hybrid LLM-ML System for Pediatric Appendicitis

arXiv:2606. 19183v1 Announce Type: cross Abstract: Large language models (LLMs) can make clinical decision support more accessible by interpreting free-text documentation, but their direct use as diagnostic engines is limited by sensitivity to prompts, information order, and plausible but incorrect outputs.

By Soheyl Bateni, Maryam Abdolali

llms 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

arXiv AI

Jun 10

MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction

arXiv:2603. 02221v2 Announce Type: replace-cross Abstract: In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods.

By Zizheng Zhang, Yiming Li, Justin Xu, Jinyu Wang, Rui Wang, Lei Song, Jiang Bian, David W Eyre, Jingjing Fu

llms benchmarks safety

arXiv Machine Learning

Jun 24

HyMaTE: A Hybrid Mamba and Transformer Model for EHR Representation Learning

arXiv:2509. 24118v2 Announce Type: replace Abstract: Electronic health Records (EHRs) have become a cornerstone in modern-day healthcare.

By Md Mozaharul Mottalib, Thao-Ly T. Phan, Rahmatollah Beheshti

llms efficiency safety

arXiv Machine Learning

Jun 5

HoT-SSM:Higher-order Temporal Knowledge Graph Reasoning with State Space Models for Health Care

arXiv:2606. 05994v1 Announce Type: new Abstract: Medical knowledge graphs (MKGs) infused with clinical knowledge have been increasingly used to model electronic health records (EHRs) to support interpretable predictions in healthcare domain.

By Thummaluru Siddartha Reddy, Vempalli Naga Sai Saketh, Yash Punjabi, Mahesh Chandran

benchmarks

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 AI

Jun 29

Chronic Kidney Disease Prognosis Prediction Using Transformer

arXiv:2511. 02340v3 Announce Type: replace Abstract: Chronic Kidney Disease (CKD) affects nearly 10\% of the global population and often progresses to end-stage renal failure.

By Yohan Lee, Dong Gyun Kang, SeHoon Park, Sa-Yoon Park, Kwangsoo Kim

llms nlp fine-tuning efficiency multimodal safety

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