![[2602.22228] Patient-Centered, Graph-Augmented Artificial Intelligence-Enabled Passive Surveillance for Early Stroke Risk Detection in High-Risk Individuals](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.22228] Patient-Centered, Graph-Augmented Artificial Intelligence-Enabled Passive Surveillance for Early Stroke Risk Detection in High-Risk Individuals
Summary
This article presents a novel AI-enabled passive surveillance system designed to detect early stroke risk in high-risk individuals, particularly those with diabetes, by analyzing patient-reported symptoms.
Why It Matters
Stroke remains a leading cause of death and disability globally. Early detection can significantly improve outcomes, making this research crucial for enhancing patient care. By utilizing patient-reported data and advanced machine learning techniques, the study addresses the critical gap in stroke risk recognition, potentially transforming clinical practices.
Key Takeaways
- The study developed a passive surveillance system for early stroke risk detection.
- Utilizes patient-reported symptoms and a dual machine learning pipeline for analysis.
- Achieved high specificity and positive predictive value while maintaining good sensitivity.
- Focuses on minimizing false alerts to enhance clinical utility.
- Highlights the importance of patient language in identifying stroke risk patterns.
Computer Science > Machine Learning arXiv:2602.22228 (cs) [Submitted on 7 Feb 2026] 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 View a PDF of the paper titled Patient-Centered, Graph-Augmented Artificial Intelligence-Enabled Passive Surveillance for Early Stroke Risk Detection in High-Risk Individuals, by Jiyeong Kim and 13 other authors View PDF 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 po...
