{"slug": "hermes-contrast-aware-knowledge-graph-reasoning-from-clinical-notes-for-patient", "title": "HERMES: Contrast-Aware Knowledge Graph Reasoning from Clinical Notes for Patient Outcome Prediction", "summary": "Researchers proposed HERMES, a graph-based framework that predicts patient outcomes using only clinical text, building personalized knowledge graphs via Large-Language-Model-guided extraction with Contrastive Logic Modeling and synthesizing patient representations through a Graph Attention Network. Experiments on MIMIC-III and MIMIC-IV for in-hospital mortality and 30-day readmission prediction show HERMES consistently outperforms strong text-only baselines, according to the arXiv paper (arXiv:2609.20825v1). The findings indicate that explicit relational modeling with Contrastive Logic Modeling significantly advances predictive performance.", "body_md": "arXiv:2609.20825v1 Announce Type: new \nAbstract: Clinical predictive models often rely on structured Electronic Health Record data, such as time-series and procedure codes. While recent approaches have begun leveraging unstructured clinical notes, they typically encode them as flat sequences, which may lose explicit relational and temporal structure present in clinical narratives. In response, we propose HERMES, a graph-based framework that operates exclusively on clinical text while preserving clinical relationships. This approach builds on two key ideas. First, personalized Knowledge Graphs (KGs) are constructed through Large-Language-Model-guided extraction from clinical notes with Contrastive Logic Modeling that explicitly captures temporal dynamics and treatment failures and changes in outcomes. Second, a Graph Attention Network synthesizes patient representations through graph-based learning over the KGs. Experiments on MIMIC-III and MIMIC-IV for in-hospital mortality and 30-day readmission prediction show that HERMES consistently outperforms strong text-only baselines. Our findings demonstrate that explicit relational modeling with Contrastive Logic Modeling significantly advances predictive performance.", "url": "https://wpnews.pro/news/hermes-contrast-aware-knowledge-graph-reasoning-from-clinical-notes-for-patient", "canonical_source": "https://arxiv.org/abs/2609.20825", "published_at": "2026-09-21 04:00:00+00:00", "updated_at": "2026-09-21 04:23:20.816460+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-research", "neural-networks"], "entities": ["HERMES", "MIMIC-III", "MIMIC-IV", "Graph Attention Network", "Contrastive Logic Modeling", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/hermes-contrast-aware-knowledge-graph-reasoning-from-clinical-notes-for-patient", "markdown": "https://wpnews.pro/news/hermes-contrast-aware-knowledge-graph-reasoning-from-clinical-notes-for-patient.md", "text": "https://wpnews.pro/news/hermes-contrast-aware-knowledge-graph-reasoning-from-clinical-notes-for-patient.txt", "jsonld": "https://wpnews.pro/news/hermes-contrast-aware-knowledge-graph-reasoning-from-clinical-notes-for-patient.jsonld"}}