HERMES: Contrast-Aware Knowledge Graph Reasoning from Clinical Notes for Patient Outcome Prediction

Not provided in the content

knowledge graphsclinical predictioncontrastive learninggraph neural networks

2609.20825

Builder Relevance

80%

1h ago

Abstract

HERMES is a graph-based framework that enhances clinical predictive models by leveraging unstructured clinical notes while preserving relational and temporal structures.

Reality Card

Core Claim

HERMES outperforms strong text-only baselines in predicting in-hospital mortality and 30-day readmissions by utilizing personalized Knowledge Graphs and Contrastive Logic Modeling.

Method / Result

HERMES consistently outperforms strong text-only baselines in predictive performance.

Limitations

The paper does not specify the authors, which may hinder reproducibility and verification of results.

Paper to code

Verified implementation resources so builders can test the paper’s claims instead of stopping at the abstract.

No verified implementation link has been attached yet. AIBuzzHub will keep this panel separate from unverified search results.

← Back to all papers