Document Type

Post-Print

Publication Date

2017

Subjects

Brain damage -- Models, System analysis, Brain damage -- Medical statistics -- Analysis, Machine learning

Abstract

This paper reports the analysis of data on traumatic brain injury using a probabilistic graphical modeling technique known as reconstructability analysis (RA). The analysis shows the flexibility, power, and comprehensibility of RA modeling, which is well-suited for mining biomedical data. One finding of the analysis is that education is a confounding variable for the Digit Symbol Test in discriminating the severity of concussion; another - and anomalous - finding is that previous head injury predicts improved performance on the Reaction Time test. This analysis was exploratory, so its findings require follow-on confirmatory tests of their generalizability.

Description

This is an Accepted Manuscript of an article published by IEEE in 2017 in IEEE Symposium Series on Computational Intelligence (SSCI). The definitive version is available here: https://doi.org/10.1109/SSCI.2017.8280843

DOI

10.1109/SSCI.2017.8280843

Persistent Identifier

https://archives.pdx.edu/ds/psu/26693

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