MedGate-Fusion: Integrating First-Encounter Semantic Narratives and Physiological Biomarkers for Prospective Stroke Risk Stratification

Abstract:Prospective stroke risk stratification in primary care is challenging because early risk signals are distributed across routine biomarkers and unstructured clinical narratives. We propose MedGate-Fusion, a multi-modal gated architecture that integrates transformer-based embeddings of first-encounter narratives with ten routinely recorded risk markers. We used electronic medical record data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Starting from 808,921 encounter-level observations, we constructed a first-encounter cohort and retained 102,736 unique patient records with non-empty narratives and sufficient data to evaluate a five-year stroke outcome. To reduce explicit target leakage from diagnostic mentions in notes, we applied dictionary-based redaction of stroke-related terms prior to semantic encoding.

7 pages,2 tables, 2 figures. Submitted to EMBC 2026

Artificial Intelligence (cs.AI)

arXiv:2609.25272 [cs.AI]

(or arXiv:2609.25272v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.25272 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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