Introduction Over the last decade advancements in computer processing have enabled the
application of machine learning (ML) to complex medical problems. Convolutional neural …

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

Introduction Traumatic brain injury (TBI) is a heterogeneous condition with a broad spectrum
of injury severity, pathophysiological processes and variable outcomes. For moderate-to …

One of the main challenges in traumatic brain injury (TBI) patients is to achieve an early and
definite prognosis. Despite the recent development of algorithms based on artificial …

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

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 …

Abstract Background and Aims Traumatic brain injury (TBI) is a widespread global health
issue with significant economic consequences. However, no existing model exists to predict …

Background: Traumatic brain injury (TBI) is a leading cause of death and disability
worldwide. The use of machine learning (ML) has emerged as a key advancement in TBI …

Traumatic brain injury (TBI) is a significant healthcare concern in several countries,
accounting for a major burden of morbidity, mortality, disability, and socioeconomic losses …

Mild traumatic brain injury (mTBI) is a common heterogeneous neurological disorder with a
wide range of possible clinical outcomes. Accurate prediction of outcome is desirable for …