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The accuracy of artificial neural networks in predicting long-term outcome after traumatic brain injury
Journal article   Peer reviewed

The accuracy of artificial neural networks in predicting long-term outcome after traumatic brain injury

Mary E Segal, Philip H Goodman, Richard Goldstein, Walter Hauck, John Whyte, John W Graham, Marcia Polansky and Flora M Hammond
The journal of head trauma rehabilitation, v 21(4)
01 Jul 2006
DOI: https://doi.org/10.1097/00001199-200607000-00003
PMID: 16915007
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

OBJECTIVEThis study compared the accuracy of artificial neural networks to multiple regression and classification and regression trees in predicting outcomes of 1,644 patients in the Traumatic Brain Injury Model Systems database 1 year after injury. METHODSData from rehabilitation admission were used to predict discharge scores on the Functional Independence Measure, the Disability Rating Scale, and the Community Integration Questionnaire. RESULTSArtificial neural networks did not demonstrate greater accuracy in predicting outcomes than did the more widely used method of multiple regression. Both of these methods outperformed classification and regression trees. CONCLUSIONBecause of the sophisticated form of multiple regression with splines that was used, firm conclusions are limited about the relative accuracy of artificial neural networks compared to more widely used forms of multiple regression.

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21 Record Views
19 citations in Web of Science
22 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

InCites Highlights

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Collaboration types
Domestic collaboration
Web of Science research areas
Clinical Neurology
Rehabilitation
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