Patient-Centered, Graph-Augmented Artificial Intelligence-Enabled Passive Surveillance for Early Stroke Risk Detection in High-Risk Individuals

• URL: http://arxiv.org/abs/2602.22228v1
• Date: Sat, 07 Feb 2026 21:34:56 GMT
• Title: Patient-Centered, Graph-Augmented Artificial Intelligence-Enabled Passive Surveillance for Early Stroke Risk Detection in High-Risk Individuals
• Authors: Jiyeong Kim, Stephen P. Ma, Nirali Vora, Nicholas W. Larsen, Julia Adler-Milstein, Jonathan H. Chen, Selen Bozkurt, Abeed Sarker, Juhee Cho, Jindeok Joo, Natali Pageler, Fatima Rodriguez, Christopher Sharp, Eleni Linos,
• Abstract summary: Stroke affected millions annually, yet poor symptom recognition often delayed care-seeking.<n>We developed a passive surveillance system for early stroke risk detection using patient-reported symptoms.<n>Patient-reported language alone supported high-precision, low-burden early stroke risk detection.
• Score: 9.880277462203361
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Stroke affected millions annually, yet poor symptom recognition often delayed care-seeking. To address risk recognition gap, we developed a passive surveillance system for early stroke risk detection using patient-reported symptoms among individuals with diabetes. Constructing a symptom taxonomy grounded in patients own language and a dual machine learning pipeline (heterogeneous GNN and EN/LASSO), we identified symptom patterns associated with subsequent stroke. We translated findings into a hybrid risk screening system integrating symptom relevance and temporal proximity, evaluated across 3-90 day windows through EHR-based simulations. Under conservative thresholds, intentionally designed to minimize false alerts, the screening system achieved high specificity (1.00) and prevalence-adjusted positive predictive value (1.00), with good sensitivity (0.72), an expected trade-off prioritizing precision, that was highest in 90-day window. Patient-reported language alone supported high-precision, low-burden early stroke risk detection, that could offer a valuable time window for clinical evaluation and intervention for high-risk individuals.

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