Objectives: This study aims to investigate whether the machine learning algorithms could
provide an optimal early mortality prediction method compared with other scoring systems …

Background Early and accurate identification of sepsis patients with high risk of in-hospital
death can help physicians in intensive care units (ICUs) make optimal clinical decisions …

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

Purpose: Traumatic brain injury (TBI) generally causes mortality and disability, particularly in
children. Machine learning (ML) is a computer algorithm, applied as a clinical prediction tool …

Purpose A reliable tool for outcome prognostication in severe traumatic brain injury (TBI)
would improve intensive care unit (ICU) decision-making process by providing objective …

Background: Traumatic brain injury (TBI) represents a significant global health issue; the
traditional tools such as the Glasgow Coma Scale (GCS) and Abbreviated Injury Scale (AIS) …

Prognosis of the long-term functional outcome of traumatic brain injury is essential for
personalized management of that injury. Nonetheless, accurate prediction remains …

Background Traumatic brain injuries (TBI) are associated with high risk of morbidity and
mortality. Early outcome prediction in patients with TBI require reliable data input and stable …

Abstract Background The Glasgow Coma Scale (GCS) is the most widely accepted scale for
assessing levels of consciousness, clinical status, as well as prognosis of traumatic brain …

When a patient is admitted to the intensive care unit (ICU) after a traumatic brain injury (TBI),
an early prognosis is essential for baseline risk adjustment and shared decision making. TBI …