Article
Mining Data on Traumatic Brain Injury with Reconstructability Analysis
Systems Science Faculty Publications and Presentations
Sponsor
This material is based in part upon work supported by the U.S. Army Contracting Command, Aberdeen Proving Ground, Natick Contracting Division, through a contract awarded to Stanford University (W911 QY-14-C- 0086), a subcontract awarded to the Brain Trauma Foundation, and a secondtier subcontract awarded to Portland State University.
Document Type
Post-Print
Publication Date
1-1-2017
Subjects
- Brain damage -- Models,
- System analysis,
- Brain damage -- Medical statistics -- Analysis,
- Reconstructability Analysis,
- Information Theory,
- Probabilistic graphical modeling,
- Multivariate analysis discrete multivariate modeling,
- Data mining
Disciplines
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.
DOI
10.1109/SSCI.2017.8280843
Persistent Identifier
https://archives.pdx.edu/ds/psu/26693
Citation Information
Martin Zwick, Nancy Carney and Rosemary Nettleton. "Mining Data on Traumatic Brain Injury with Reconstructability Analysis" (2017) Available at: http://works.bepress.com/martin_zwick/89/
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