← Back to all news
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.

Related stories

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
llmsreinforcement-learningbenchmarks
More like this →
arXiv Machine Learning
Oct 7

Cite What You Explore: Budget-Aware LLM Reasoning over Medical KGs with Verifiable Evidence

The paper introduces BAR, a Budget‑Aware LLM Reasoning framework that enhances post‑discharge risk prediction by integrating external medical knowledge graphs (KGs). BAR refines KGs into disease‑specific evidence graphs with support scores and provenance, then uses an LLM to plan, navigate, and verify evidence within a patient‑specific budget. Experiments on MIMIC‑III and MIMIC‑IV across eight diseases show BAR improves AUPRC by 3.4 points, raises citation precision from 59.8% to 77.9%, and uses only 62‑65% of the allotted budget.

By Chen Chen, Dongjie Wang, Mei Liu, Zijun Yao
llms
More like this →
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
ragefficiencybenchmarks
More like this →
arXiv Machine Learning
Sep 14

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-learningfine-tuningsafety
More like this →
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
nlpmultimodal
More like this →
arXiv Machine Learning
Oct 15

MAEM: Mitigating Recency Bias via a Replay-Free, Capacity-Controlled Dynamic Mixture of Experts for Class-Incremental Graph Learning

arXiv:2610.20238v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) excel on graph-structured data but suffer catastrophic forgetting when classes arrive sequentially. In class-incremental g...

By Junlin Chen, Wenhao Wang, Junchao Xu, Qingsong Yao, Fei Xue, Li Sun, Xiao Wang, Ruijie Wang
fine-tuningbenchmarkssafety
More like this →