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, Flora M. Hammond

Research output: Contribution to journalArticle

18 Scopus citations

Abstract

OBJECTIVE: This study compared the accuracy of artificial neural networks to multiple regression and classification and regression trees in predicting outcomes of 1644 patients in the Traumatic Brain Injury Model Systems database 1 year after injury. METHODS: Data from rehabilitation admission were used to predict discharge scores on the Functional Independence Measure, the Disability Rating Scale, and the Community Integration Questionnaire. RESULTS: Artificial 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. CONCLUSION: Because 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.

Original languageEnglish (US)
Pages (from-to)298-314
Number of pages17
JournalJournal of Head Trauma Rehabilitation
Volume21
Issue number4
DOIs
StatePublished - Jul 1 2006
Externally publishedYes

Keywords

  • Classification and regression trees
  • Multiple regression
  • Neural networks
  • Outcomes
  • Traumatic brain injury

ASJC Scopus subject areas

  • Physical Therapy, Sports Therapy and Rehabilitation
  • Rehabilitation
  • Clinical Neurology

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