Classification tree modeling to identify severe and moderate vehicular injuries in young and middle-aged adults

Artificial Intelligence in Medicine

Volumn 45, Issue 1, 2009, Pages 1-10

  Scheetz, Linda J ^a   Zhang, Juan ^b   Kolassa, John ^c  

^a NEW YORK UNIVERSITY   (United States)

^b BRISTOL MYERS SQUIBB   (United States)

^c RUTGERS UNIVERSITY   (United States)

Author keywords

Artificial intelligence; Classification and regression tree analysis; Data mining; Decision support techniques; Emergency Medical Services; Injury Severity Score; Triage

Indexed keywords

CLASSIFICATION AND REGRESSION TREE ANALYSIS; DATA MINING; DECISION SUPPORT TECHNIQUES; EMERGENCY MEDICAL SERVICES; INJURY SEVERITY SCORE; TRIAGE;

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL FLUID DYNAMICS; CRASHWORTHINESS; DATABASE SYSTEMS; DECISION SUPPORT SYSTEMS; HOSPITALS; INFORMATION MANAGEMENT; LAW ENFORCEMENT; MEDICAL COMPUTING; MOTORS; REGRESSION ANALYSIS; VEHICLES;

TREES (MATHEMATICS);

ADULT; AGE DISTRIBUTION; ARTICLE; DATA BASE; DISEASE CLASSIFICATION; DISEASE SEVERITY; GROUPS BY AGE; HUMAN; MAJOR CLINICAL STUDY; POLICE; PREDICTOR VARIABLE; PRIORITY JOURNAL; SCORING SYSTEM; SENSITIVITY AND SPECIFICITY; TRAFFIC ACCIDENT;

ACCIDENTS, TRAFFIC; ADOLESCENT; ADULT; ALGORITHMS; DATABASES, FACTUAL; HUMANS; MIDDLE AGED; MODELS, THEORETICAL; SEVERITY OF ILLNESS INDEX; WOUNDS AND INJURIES;

EID: 60349129475     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2008.11.002     Document Type: Article

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