Please use this identifier to cite or link to this item: https://doi.org/10.1089/neu.2006.0113
Title: Hybrid outcome prediction model for severe traumatic brain injury
Authors: Boon, C.P.
Kuralmani, V.
Joshi, R. 
Hongli, Y.
Kah, K.L.
Beng, T.A.
Li, J.
Tze, Y.L. 
Ng, I.
Keywords: Adult brain injury
Assessment tools
Bayesian network
Decision tree
Discriminant analysis
Human studies
Logistic regression
Neural network
Outcome measures
Traumatic brain injuries
Issue Date: 2007
Source: Boon, C.P., Kuralmani, V., Joshi, R., Hongli, Y., Kah, K.L., Beng, T.A., Li, J., Tze, Y.L., Ng, I. (2007). Hybrid outcome prediction model for severe traumatic brain injury. Journal of Neurotrauma 24 (1) : 136-146. ScholarBank@NUS Repository. https://doi.org/10.1089/neu.2006.0113
Abstract: Numerous studies addressing different methods of head injury prognostication have been published. Unfortunately, these studies often incorporate different head injury prognostication models and study populations, thus making direct comparison difficult, if not impossible. Furthermore, newer artificial intelligence tools such as machine learning methods have evolved in the field of data analysis, alongside more traditional methods of analysis. This study targets the development of a set of integrated prognostication model combining different classes of outcome and prognostic factors. Methodologies such as discriminant analysis, logistic regression, decision tree, Bayesian network, and neural network were employed in the study. Several prognostication models were developed using prospectively collected data from 513 severe closed head-injured patients admitted to the Neurocritical Unit at National Neuroscience Institute of Singapore, from April 1999 to February 2003. The correlation between prognostic factors at admission and outcome at 6 months following injury was studied. Overfitting error, which may falsely distinguish different outcomes, was compared graphically. Tenfold cross-validation technique, which reduces overfitting error, was used to validate outcome prediction accuracy. The overall prediction accuracy achieved ranged from 49.79% to 81.49%. Consistently high outcome prediction accuracy was seen with logistic regression and decision tree. Combining both logistic regression and decision tree models, a hybrid prediction model was then developed. This hybrid model would more accurately predict the 6-month post-severe head injury outcome using baseline admission parameters. © Mary Ann Liebert, Inc.
Source Title: Journal of Neurotrauma
URI: http://scholarbank.nus.edu.sg/handle/10635/39258
ISSN: 08977151
DOI: 10.1089/neu.2006.0113
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

35
checked on Dec 6, 2017

WEB OF SCIENCETM
Citations

31
checked on Nov 22, 2017

Page view(s)

63
checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.