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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.22228 |
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| _version_ | 1866910033452728320 |
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| author | Kim, Jiyeong Ma, Stephen P. Vora, Nirali Larsen, Nicholas W. Adler-Milstein, Julia Chen, Jonathan H. Bozkurt, Selen Sarker, Abeed Cho, Juhee Joo, Jindeok Pageler, Natali Rodriguez, Fatima Sharp, Christopher Linos, Eleni |
| author_facet | Kim, Jiyeong Ma, Stephen P. Vora, Nirali Larsen, Nicholas W. Adler-Milstein, Julia Chen, Jonathan H. Bozkurt, Selen Sarker, Abeed Cho, Juhee Joo, Jindeok Pageler, Natali Rodriguez, Fatima Sharp, Christopher Linos, Eleni |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_22228 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Patient-Centered, Graph-Augmented Artificial Intelligence-Enabled Passive Surveillance for Early Stroke Risk Detection in High-Risk Individuals Kim, Jiyeong Ma, Stephen P. Vora, Nirali Larsen, Nicholas W. Adler-Milstein, Julia Chen, Jonathan H. Bozkurt, Selen Sarker, Abeed Cho, Juhee Joo, Jindeok Pageler, Natali Rodriguez, Fatima Sharp, Christopher Linos, Eleni Machine Learning 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. |
| title | Patient-Centered, Graph-Augmented Artificial Intelligence-Enabled Passive Surveillance for Early Stroke Risk Detection in High-Risk Individuals |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.22228 |