MedGate-Fusion：整合首次就诊语义叙述与生理生物标志物用于前瞻性卒中风险分层
MedGate-Fusion: Integrating First-Encounter Semantic Narratives and Physiological Biomarkers for Prospective Stroke Risk Stratification
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中文总结 AI 辅助
针对初级保健中卒中风险分层难的问题，提出MedGate-Fusion多模态门控架构，融合首次就诊叙述嵌入与十项常规风险标志物，利用CPCSSN数据构建队列并处理目标泄漏，实现前瞻性风险分层。
中文摘要 AI 辅助
在初级保健中进行前瞻性卒中风险分层具有挑战性，因为早期风险信号分布在常规生物标志物和非结构化的临床叙述中。我们提出了MedGate-Fusion，一种多模态门控架构，将基于transformer的首次就诊叙述嵌入与十项常规记录的风险标志物相结合。我们使用了加拿大初级保健哨点监测网络（CPCSSN）的电子病历数据。从808,921条就诊级观察数据出发，我们构建了首次就诊队列，并保留了102,736条具有非空叙述且数据充足的患者记录，以评估五年卒中结局。为了减少笔记中诊断提及导致的显式目标泄漏，我们在语义编码前应用了基于词典的卒中相关术语删除。
英文摘要
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.
发表机构
- University of Toronto（多伦多大学）
- Toronto Metropolitan University（多伦多都会大学）
机构由 AI 辅助整理，请以论文原文为准。
