Journal article
The accuracy of artificial neural networks in predicting long-term outcome after traumatic brain injury
The journal of head trauma rehabilitation, v 21(4)
01 Jul 2006
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.
Metrics
Details
- Title
- The accuracy of artificial neural networks in predicting long-term outcome after traumatic brain injury
- Creators
- Mary E Segal - Research Center for Health Care Decision-making, Inc.Philip H Goodman - University of Nevada RenoRichard Goldstein - Harvard University ,Walter Hauck - Thomas Jefferson UniversityJohn Whyte - Yeshiva UniversityJohn W Graham - Pennsylvania State UniversityMarcia Polansky - Drexel UniversityFlora M Hammond - Carolinas Healthcare System
- Publication Details
- The journal of head trauma rehabilitation, v 21(4)
- Publisher
- Lippincott
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- [Retired Faculty]
- Web of Science ID
- WOS:000239109100003
- Scopus ID
- 2-s2.0-33747506451
- Other Identifier
- 991019168034504721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Clinical Neurology
- Rehabilitation