A5096742769- Acute Ischemic Stroke Management
- Machine Learning in Healthcare
- Stroke Rehabilitation and Recovery
- Artificial Intelligence in Healthcare
- Cerebrovascular and Carotid Artery Diseases
- Intracerebral and Subarachnoid Hemorrhage Research
Jordan University of Science and Technology
2024
Posterior Circulation Syndrome (PCS) presents a diagnostic challenge characterized by its variable and nonspecific symptoms. Timely accurate diagnosis is crucial for improving patient outcomes. This study aims to enhance the early of PCS employing clinical demographic data machine learning. approach targets significant research gap in field stroke management.
Abstract Background: Accurate prediction of mortality following an ischemic stroke is essential for tailoring personalized treatment strategies. This study evaluates the effectiveness machine learning models in predicting one-year after stroke. Methods: Five were trained using data from a national registry, with logistic regression demonstrating highest performance. The SHapley Additive exPlanations (SHAP) analysis explained model’s outcomes and defined influential predictive factors....
Stroke-associated Hospital Acquired Pneumonia (HAP) significantly impacts patient outcomes. This study explores the utility of machine learning models in predicting HAP stroke patients, leveraging national registry data and SHapley Additive exPlanations (SHAP) analysis to identify key predictive factors.