@article{ref1,
title="Pairwise, ordinal outlier detection of traumatic brain injuries",
journal="Lecture notes in computer science",
year="2018",
author="Higger, Matt and Shenton, Martha and Bouix, Sylvain",
volume="10670",
number="",
pages="100-110",
abstract="Because mild Traumatic Brain Injuries (mTBI) are heterogeneous, classification methods perform outlier detection from a model of healthy tissue. Such a model is challenging to construct. Instead, we utilize region-specific pairwise (person-to-person) comparisons. Each person-region is characterized by a distribution of Fractional Anisotropy and comparisons are made via Median, Mean, Bhattacharya and Kullback-Liebler distances. Additionally, we examine an ordinal decision rule which compares a subject's n<sup>th</sup> most atypical region to a healthy control's. Ordinal comparison is motivated by mTBI's heterogeneity; each mTBI has some set of damaged tissue which is not necessarily spatially consistent. These improvements correctly distinguish Persistent Post-Concussive Symptoms in a small dataset but achieve only a.74 AUC in identifying mTBI subjects with milder symptoms. Finally, we perform subject-specific simulations which characterize which injuries are detected and which are missed.<p /> <p>Language: en</p>",
language="en",
issn="0302-9743",
doi="10.1007/978-3-319-75238-9_9",
url="http://dx.doi.org/10.1007/978-3-319-75238-9_9"
}