Reported areas under the receiver operating characteristic curve varied by prediction task, ranging from 0.75 to 0.96 for acute diagnosis models and from 0.75 to 0.97 for onset risk prediction models; ...

Background: Since its inception, the National Institutes of Health Stroke Scale (NIHSS) has defined severity for acute ischemic stroke (AIS). The simple risk calculator for arteriovenous malformation ...

Abstract: Stroke is a major reason for disability and mortality across the globe, making initial prediction and intervention critical t o reducing its impact. This project leverages machine learning ...

Abstract: Stroke, a medical emergency that occurs due to the interruption of flow of blood to a part of brain because of bleeding or blood clots. Worldwide, it is the second major reason for deaths ...

Machine learning (ML) techniques are widely employed across various domains to achieve accurate predictions. This study assessed the effectiveness of ML in predicting early mortality risk among ...

Background: The importance of being physically active and avoiding staying in bed has been recognized in stroke rehabilitation. However, studies have pointed out that stroke patients admitted to ...

Background: The key to effective stroke management is timely diagnosis and triage. Machine learning (ML) methods developed to assist in detecting stroke have focused on interpreting detailed clinical ...

Mechanical thrombectomy greatly improves stroke outcomes. Nonetheless, some patients fall short of full recovery despite good reperfusion. The purpose of this study was to develop machine learning (ML ...

Owkin's best-performing AI model relied on the typical factors of heart disease, such as age, medical history and smoking status, but it also identified less-common details that could one day lead ...