arXiv AI

Import What You Need: Learning When and How to Augment EHR Graphs with External Knowledge

The paper introduces ReTA, a reinforcement‑learning framework that dynamically decides how to augment electronic health record (EHR) graphs with external knowledge graphs (KGs) on a per‑visit basis. ReTA offers three actions—Soft Import, Hard Import, and Skip—allowing it to enrich node features, graft compact KG subgraphs, or leave the graph unchanged depending on the patient’s evolving state and a budget constraint. Experiments on MIMIC‑III and MIMIC‑IV demonstrate that ReTA consistently outperforms strong baselines, transfers across datasets and KGs, and provides interpretable augmentation patterns while maintaining efficiency.

arXiv AI

Sep 7

REFINE: LLM Refinement over Budgeted Text-Attributed Graphs for Personalized Medical Concept Representation

REFINE is a framework that refines medical concept representations by creating patient‑specific temporal graphs from a global text‑attributed knowledge graph. It uses a reinforcement learning policy to allocate a personalized graph expansion budget for each observed code, then processes the resulting graph with a heterogeneous GNN and a frozen LLM that refines representations via graph‑aware soft prompts. Experiments on MIMIC‑III and MIMIC‑IV demonstrate that REFINE consistently improves various EHR prediction backbones, surpasses strong baselines, and shows robust gains across ablation studies, KG selection, and data insufficiency scenarios.

By Mohsen Nayebi Kerdabadi, Arya Hadizadeh Moghaddam, Dongjie Wang, Zijun Yao

llms reinforcement-learning benchmarks

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 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 AI

Jul 22

Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented Generation

arXiv:2607. 18270v1 Announce Type: new Abstract: While Electronic Health Records (EHRs) offer a wealth of clinical data, effectively augmenting a patient's records with heterogeneous external knowledge to predict the patient's clinical risk remains a significant challenge.

By Kyunghoon Jeon, Youmin Ko, Woohwan Jung, Hyunjoon Kim

rag efficiency benchmarks

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 Machine Learning

4d ago

Reinforcement Learning over Patient Trajectories for Clinical Reasoning in EHR Foundation Models

The paper proposes a reinforcement learning (RL) fine‑tuning framework for electronic health record (EHR) foundation models, treating them as generative policies over patient trajectories. By framing clinical prediction tasks such as hospital readmission as event‑conditioned, time‑windowed reasoning problems and designing time‑aware, rollout‑sensitive rewards, the authors show that RL fine‑tuning consistently outperforms pre‑trained backbones and strong baselines. The approach enables smaller models to surpass larger pre‑trained models in data‑limited settings, induces positive transfer across tasks, and produces trajectories with stronger structural and semantic alignment to ground truth, improving downstream utility.

By Yuxin Xiao, Sheng Zhang, Chandan Singh, Tristan Naumann, Hoifung Poon, Jianfeng Gao, Xiaodong Liu

reinforcement-learning fine-tuning safety

arXiv Machine Learning

Jun 8

GraphWalker: Patient Analogy Meets Information Gain for Clinical Reasoning with Large Language Models

arXiv:2604. 06684v2 Announce Type: replace Abstract: Clinical reasoning over electronic health records (EHRs) is a fundamental yet challenging task in modern healthcare.

By Yue Fang, Weibin Liao, Yuxin Guo, Jiaran Gao, Hongxin Ding, Jinyang Zhang, Xinke Jiang, Zhibang Yang, Junfeng Zhao, Yasha Wang, Liantao Ma

llms agents benchmarks

arXiv Machine Learning

Aug 4

EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Records

arXiv:2506. 04831v3 Announce Type: replace Abstract: Forecasting how a patient's condition is likely to evolve, including possible deterioration, recovery, treatment needs, and care transitions, could support more proactive and personalized care, but requires modeling heterogeneous and longitudinal electronic health record (EHR) data.

By Chantal Pellegrini, Ege \"Ozsoy, David Bani-Harouni, Matthias Keicher, Nassir Navab

nlp multimodal

arXiv Computation and Language

4d ago

Repair Before Reinforce: Context-Augmented Knowledge Graph Reasoning for Multi-Hop Question Answering

arXiv:2609.12230v1 Announce Type: new Abstract: Question-answering often requires reasoning across multiple connected facts rather than retrieving a single isolated relation. Knowledge graphs (KGs) p...

By Tharaka D. Fonseka, Niraj K. Jha

llms nlp reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 8

PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning

arXiv:2605. 07267v2 Announce Type: replace Abstract: Personalized healthcare decisions require reasoning about how physiological and behavioral variables influence an individual patient over time.

By Elahe Khatibi, Ziyu Wang, Saba A. Farahani, Di Huang, Hung Cao, Ramesh Jain, Amir M. Rahmani

benchmarks

arXiv AI

Jun 3

Traj-Evolve: A Self-Evolving Multi-Agent System for Patient Trajectory Modeling in Lung Cancer Early Detection

arXiv:2606. 02812v1 Announce Type: new Abstract: Modeling patient trajectories from longitudinal electronic health records (EHRs) requires reasoning over sparse, noisy, and long-context multimodal sequences.

By Sihang Zeng, Matthew Thompson, Ruth Etzioni, Meliha Yetisgen

llms agents reinforcement-learning fine-tuning multimodal

arXiv AI

Aug 25

Clinical Graph-JEPA: Predictive Patient-State Knowledge Graphs for Cognitive Decision Support

The paper introduces Clinical Graph-JEPA, a framework for building and refining predictive patient-state knowledge graphs from clinical records. It combines multi-agent relation proposal, ontology-aware normalization, deterministic evidence scoring, and JEPA-based latent refinement to construct evidence-scored graphs from MIMIC-IV data and recover missing clinical relations. Experiments show that injecting discharge-note representations into note-grounded entities boosts leave-one-out MRR by 31% relative improvement.

By Kushagra Yadav, Nalin Prabhath, Amit Lamba, Goeun Han, Yining Mao

rag agents

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