arXiv AI
Consistent Relexicalization of Clinical Documents using Graph-Based Approach
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arXiv AI
Aug 25
Search Broadly, Seek Evidence on Both Sides, Decide Narrowly: Evidence-Admissible GraphRAG for Longitudinal Clinical Event Verification
arXiv:2608.22062v1 Announce Type: new Abstract: Longitudinal clinical event-relation verification determines whether a patient record supports a specified relation among two or more clinical events....
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.
arXiv AI
Jul 17
MedBeads: An AI-Native Clinical Context Graph Built from Immutable Beads and Reconstructable Clinical Links
arXiv:2602. 01086v3 Announce Type: replace Abstract: Generative AI can encode substantial medical knowledge, but patient-specific answers remain constrained by the context supplied at inference time.
arXiv AI
Jul 10
Drift-Aware Temporal Graph Rewiring (DATGR) for Adaptive Semantic Modeling in Biomedical Text
arXiv:2607. 08490v1 Announce Type: new Abstract: Biomedical language evolves rapidly as new discoveries emerge, causing traditional text models to lose semantic fidelity over time.
arXiv Machine Learning
Sep 21
HERMES: Contrast-Aware Knowledge Graph Reasoning from Clinical Notes for Patient Outcome Prediction
HERMES is a graph-based framework that uses only clinical text to predict patient outcomes. It builds personalized Knowledge Graphs from clinical notes via Large‑Language‑Model‑guided extraction and Contrastive Logic Modeling, capturing temporal dynamics and treatment changes. A Graph Attention Network then synthesizes patient representations, and experiments on MIMIC‑III and MIMIC‑IV show HERMES outperforms text‑only baselines for in‑hospital mortality and 30‑day readmission prediction.
arXiv Computer Vision
Aug 26
Graph-Supervised Hierarchical Clinical Alignment for Radiology Report Generation with Large Language Models
arXiv:2608.24121v1 Announce Type: new Abstract: Radiology report generation (RRG) has recently benefited from large language models, which substantially improve report fluency. However, clinically fa...