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

2026-09-21

AI Summary

本文提出MedGate-Fusion模型，通过整合首次就诊的语义叙述和常规生理生物标志物来解决初级护理中前瞻性卒中风险分层的问题。

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.

Problem

• prospective stroke risk stratification
• primary care
• unstructured clinical narratives
• routine biomarkers

Innovation

• multi-modal gated architecture
• transformer-based embeddings
• prospective stroke risk stratification