[Submitted on 22 Jul 2026]
Title:HERMES: Contrast-Aware Knowledge Graph Reasoning from Clinical Notes for Patient Outcome Prediction
View a PDF of the paper titled HERMES: Contrast-Aware Knowledge Graph Reasoning from Clinical Notes for Patient Outcome Prediction, by Gia-Bach Nguyen and 7 other authors
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Abstract: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.
Comments: 12 pages, 4 figures, The 15th Conference on Information Technology and its Applications
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2609.20825 [cs.CL]
(or arXiv:2609.20825v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.20825
arXiv-issued DOI via DataCite
Submission history
From: Ha Nguyen [view email] [v1] Wed, 22 Jul 2026 09:00:41 UTC (2,394 KB)
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