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


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.


CONCLUSIONS
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.
Segal
Mary E
ME
Research Center for Health Care Decision-making, Inc, Wyndmoor, PA 19038, USA. msegal@healthcaredecisions.org
Goodman
Philip H
PH
Goldstein
Richard
R
Hauck
Walter
W
Whyte
John
J
Graham
John W
JW
Polansky
Marcia
M
Hammond
Flora M
FM
D001930:Brain Injuries / Q000503:physiopathology*; D019992:Databases as Topic; D004185:Disability Evaluation; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D017063:Outcome Assessment, Health Care / Q000379:methods*; D012044:Regression Analysis; D020379:Risk Adjustment