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

Journal of Head Trauma Rehabilitation

Volumn 21, Issue 4, 2006, Pages 298-314

  Segal, Mary E ^a   Goodman, Philip H ^b   Goldstein, Richard ^c   Hauck, Walter ^d   Whyte, John ^e   Graham, John W ^f   Polansky, Marcia ^g   Hammond, Flora M ^h  

^a Res Ctr Hlth Care D Making Inc   (United States)

^b UNIVERSITY OF NEVADA   (United States)

^c HARVARD MEDICAL SCHOOL   (United States)

^d THOMAS JEFFERSON UNIVERSITY   (United States)

^e MOSS REHABILITATION RESEARCH INSTITUTE   (United States)

^f PENNSYLVANIA STATE UNIVERSITY   (United States)

^g DREXEL UNIVERSITY   (United States)

^h Carolinas Rehabilitation   (United States)

Author keywords

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

Indexed keywords

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION AND REGRESSION TREE; COMMUNITY INTEGRATION QUESTIONNAIRE; DATA BASE; DISABILITY; DISABILITY RATING SCALE; DISEASE CLASSIFICATION; FUNCTIONAL INDEPENDENCE MEASURE; HOSPITAL DISCHARGE; HUMAN; MAJOR CLINICAL STUDY; MULTIPLE REGRESSION; OUTCOME ASSESSMENT; PREDICTION; PROGNOSIS; QUESTIONNAIRE; RATING SCALE; SCORING SYSTEM; STATISTICAL ANALYSIS; TRAUMATIC BRAIN INJURY;

BRAIN INJURIES; DATABASES; DISABILITY EVALUATION; FEMALE; FOLLOW-UP STUDIES; HUMANS; MALE; NEURAL NETWORKS (COMPUTER); OUTCOME ASSESSMENT (HEALTH CARE); REGRESSION ANALYSIS; RISK ADJUSTMENT;

EID: 33747506451     PISSN: 08859701     EISSN: None     Source Type: Journal    
DOI: 10.1097/00001199-200607000-00003     Document Type: Article

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