The utilization of Artificial Intelligence (AI) and Machine Learning (ML) is paving the way for
significant strides in patient diagnosis, treatment, and prognostication in neurocritical care …

Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging
worldwide. The present study aimed to achieve the most accurate machine learning (ML) …

Defining reliable tools for early prediction of outcome is the main target for physicians to
guide care decisions in patients with brain injury. The application of machine learning (ML) …

The prediction of functional outcome after mild traumatic brain injury (mTBI) is challenging.
Conventional magnetic resonance imaging (MRI) does not do a good job of explaining the …

OBJECTIVE An estimated 1.5 million people die every year worldwide from traumatic brain
injury (TBI). Physicians are relatively poor at predicting long-term outcomes early in patients …

Aim Controversy remains high over the superiority of advanced machine learning (ML)
algorithms to conventional logistic regression (LR) in the prediction of prognosis after …

Background Accurate patient-specific predictions on return-to-work after traumatic brain
injury (TBI) can support both clinical practice and policymaking. The use of machine learning …

Early detection of diabetes is essential to prevent serious complications in patients. The
purpose of this work is to detect and classify type 2 diabetes in patients using machine …

Background: Accurate prediction of in-hospital mortality is essential for better management
of patients with traumatic brain injury (TBI). Machine learning (ML) algorithms have been …

Objective To investigate the performance of machine learning (ML) methods for predicting
outcomes from inpatient rehabilitation for subjects with TBI using a dataset with a large …