학술논문
Mining data on traumatic brain injury with reconstructability analysis
Document Type
Conference
Source
2017 IEEE Symposium Series on Computational Intelligence (SSCI) Computational Intelligence (SSCI), 2017 IEEE Symposium Series on. :1-6 Nov, 2017
Subject
Language
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.