Objectives The goal of this study is to implement machine learning (ML) algorithms to predict
mortality, non-home discharge, prolonged length of stay (LOS), prolonged length of …

Abstract BACKGROUND CONTEXT A traumatic spinal cord injury (SCI) can cause
temporary or permanent motor and sensory impairment, leading to serious short and long …

Background Spinal cord injury (SCI) is a significant public health issue, leading to physical,
psychological, and social complications. Machine learning (ML) algorithms have shown …

BACKGROUND CONTEXT Acute traumatic cervical spinal cord injury (cSCI) leads in
temporary or permanent impairment of motor function and sensation, and has devastating …

Study Design: A retrospective case-series. Objective: The study aims to use machine-
learning (ML) to predict the discharge destination of spinal cord injury (SCI) patients in the …

Background Machine learning has been applied to improve diagnosis and prognostication
of acute traumatic spinal cord injury. We investigate potential for clinical integration of …

Abstract Background Context Current prognostic tools such as the Injury Severity Score
(ISS) that predict mortality following trauma do not adequately consider the unique …

Abstract Machine learning represents a promising frontier in epidemiological research on
spine surgery. It consists of a series of algorithms that determines relationships between …

Objective: The primary aim was to develop a machine learning (ML) model to predict
rehospitalization during the first year of traumatic spinal cord injury (TSCI) and to identify top …

The study used machine learning to predict The American Spinal Injury Association
Impairment Scale (AIS) scores for newly injured spinal cord injury patients at hospital …