@article{ref1,
title="Applications of transductive spectral graph methods in a military medical concussion database",
journal="IEEE/ACM transactions on computational biology and bioinformatics",
year="2016",
author="Walker, Peter B. and Norris, Jacob N. and Tshiffiley, Anna E. and Mehalick, Melissa L. and Cunningham, Craig A. and Davidson, Ian N.",
volume="14",
number="3",
pages="534-544",
abstract="Traumatic brain injury (TBI) is one of the most common forms of neurotrauma that has affected more than 250,000 military service members over the last decade alone. While in battle, service members who experience TBI are at significant risk for the development of normal TBI symptoms, as well as risk for the development of psychological disorders such as Post-Traumatic Stress Disorder (PTSD). As such, these service members often require intense bouts of medication and therapy in order to resume full return-to-duty status. The primary aim of this study is to identify the relationship between the administration of specific medications and reductions in symptomology such as headaches, dizziness, or light-headedness. Service members diagnosed with mTBI and seen at the Concussion Restoration Care Center (CRCC) in Afghanistan were analyzed according to prescribed medications and symptomology. Here, we demonstrate that in such situations with sparse labels and small feature sets, classic analytic techniques such as logistic regression, support vector machines, naïve Bayes, random forest, decision trees, and k-nearest neighbor are not well suited for the prediction of outcomes. We attribute our findings to several issues inherent to this problem setting and discuss several advantages of spectral graph methods.<p /> <p>Language: en</p>",
language="en",
issn="1545-5963",
doi="10.1109/TCBB.2016.2591549",
url="http://dx.doi.org/10.1109/TCBB.2016.2591549"
}