Meeting Banner
Abstract #2941

Predicting symptomatic outcome in mild traumatic brain injury with support vector machines: a 1H-MRS Study

Elijah George 1,2 , Steve Roys 2 , Jiachen Zhuo 2 , Chandler Sours 2 , Joseph Rosenberg 2 , and Rao Gullapalli 2

1 Bioengineering, University of Maryland, College Park, Maryland, United States, 2 Magnetic Resonance Research Center, University of Maryland Baltimore, School of Medicine, Baltimore, Maryland, United States

Mild traumatic brain injury (mTBI) patients represent 75% of the viable TBI population. The aim of the current study is to acutely predict the symptomatic outcome of mTBI patients 6 months post injury (PI) neurometabolic measurements from magnetic resonance spectroscopy (MRS). Herein, we applied acute neurometabolic information to the support vector machine (SVM) algorithm in order to differentiate between patients with and without post concussive syndrome (PCS) 6 months PI

This abstract and the presentation materials are available to members only; a login is required.

Join Here