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

Aim This study aims to develop prediction models for in-hospital outcomes after non-surgical
treatment among patients with moderate-to-severe traumatic brain injury (TBI). Method We …

Aim This study aims to utilize machine learning (ML) and logistic regression (LR) models to
predict surgical outcomes among patients with traumatic brain injury (TBI) based on …

Recently, successful predictions using machine learning (ML) algorithms have been
reported in various fields. However, in traumatic brain injury (TBI) cohorts, few studies have …

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 …

Prognostic prediction of traumatic brain injury (TBI) in patients is crucial in clinical decision
and health care policy making. This study aimed to develop and validate prediction models …

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) …

Background The purpose of this study was to build a model of machine learning (ML) for the
prediction of mortality in patients with isolated moderate and severe traumatic brain injury …

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

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