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
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