Mining data on traumatic brain injury with reconstructability analysis

Martin Zwick, Nancy Carney, Rosemary Nettleton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
Volume2018-January
ISBN (Electronic)9781538627259
DOIs
StatePublished - Feb 2 2018
Event2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States
Duration: Nov 27 2017Dec 1 2017

Other

Other2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
CountryUnited States
CityHonolulu
Period11/27/1712/1/17

Fingerprint

Data mining
Brain
Data Mining
Education
Graphical Modeling
Probabilistic Modeling
Reaction Time
Confounding
Digit
Anomalous
Mining
Flexibility
Predict
Modeling

Keywords

  • concussion
  • data mining
  • health care analytics
  • information theory
  • machine learning
  • OCCAM
  • reconstructability analysis
  • traumatic brain injury

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Optimization

Cite this

Zwick, M., Carney, N., & Nettleton, R. (2018). Mining data on traumatic brain injury with reconstructability analysis. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings (Vol. 2018-January, pp. 1-6). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2017.8280843

Mining data on traumatic brain injury with reconstructability analysis. / Zwick, Martin; Carney, Nancy; Nettleton, Rosemary.

2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-6.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zwick, M, Carney, N & Nettleton, R 2018, Mining data on traumatic brain injury with reconstructability analysis. in 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017, Honolulu, United States, 11/27/17. https://doi.org/10.1109/SSCI.2017.8280843
Zwick M, Carney N, Nettleton R. Mining data on traumatic brain injury with reconstructability analysis. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-6 https://doi.org/10.1109/SSCI.2017.8280843
Zwick, Martin ; Carney, Nancy ; Nettleton, Rosemary. / Mining data on traumatic brain injury with reconstructability analysis. 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-6
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