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Research Article
A Critical Review for an Accurate and Dynamic Prediction for the Outcomes of Traumatic Brain Injury based on Glasgow Outcome Scale

Hamdan O. Alanazi, Abdul Hannan Abdullah and Mohammed Al Jumah

Journal of Medical Sciences, 2013, 13(4), 244-252.

Abstract

The world and every 5 min someone dies from Traumatic Brain Injury (TBI). Furthermore, it is a leading cause of death and disability in the world. Identification of patients with poor neurologic prognosis causes problem for the patients and their families. Presently, computer technology is increasingly been used and implemented in healthcare and predicting patient outcome can be useful as an aid to clinical decision making, explore possible biological mechanisms and as part of the clinical audit process. Machine learning, a branch of artificial Intelligence aims to make computer automated predictions more accurate. Neurologists need an accurate model to predict the neurologic outcome in patients with brain injury and this remains a challenge for the intensivist. A critical review on existing predictive models of traumatic brain injury is conducted in Science Direct, PubMed, Elsevier and Springer Link some other publishers. A review of related literature reveals that there is no method classified yet as being the perfect machine learning method. The review further shows that no prognostic models in TBI have yet been developed with proven results. In addition, it shows that predicting the outcomes of traumatic brain injury based on Glasgow Outcome Scale using machine learning methods is essential and needs to be improved.

ASCI-ID: 41-1151

Cited References Fulltext

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