Knowledge of the prognosis of acute spinal cord injury is critical to provide appropriate
information for clients and make the best treatment choices. Acute intervertebral disc …

Personalized medicine is a new paradigm of healthcare in which interventions are based on
individual patient characteristics rather than on “one-size-fits-all” guidelines. As …

BACKGROUND With spinal surgery rates increasing in North America, models that are able
to accurately predict which patients are at greater risk of developing complications are highly …

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

The accurate prediction of neurological outcomes in patients with cervical spinal cord injury
(SCI) is difficult because of heterogeneity in patient characteristics, treatment strategies, and …

Artificial neural networks (ANNs) have been used in a wide variety of real-world applications
and it emerges as a promising field across various branches of medicine. This review aims …

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

Background The use of machine learning has the potential to estimate the probability of a
second classification event more accurately than traditional statistical methods, and few …

As the use of machine learning algorithms in the development of clinical prediction models
has increased, researchers are becoming more aware of the deleterious effects that stem …

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