Random forest can predict 30-day mortality of spontaneous intracerebral hemorrhage with remarkable discrimination

European Journal of Neurology

Volumn 17, Issue 7, 2010, Pages 945-950

  Peng, S Y ^a,b,c   Chuang, Y C ^b   Kang, T W ^b   Tseng, K H ^d  

^a NATIONAL YANG MING UNIVERSITY   (Taiwan)

^b TAICHUNG VETERANS GENERAL HOSPITAL   (Taiwan)

^c CHUNG SHAN MEDICAL UNIVERSITY   (Taiwan)

^d Taoyuan Veterans Hospital   (Taiwan)

Author keywords

intracerebral hemorrhage; mortality; outcome; ROC curve; sensitivity and specificity

Indexed keywords

ADULT; ARTICLE; ARTIFICIAL NEURAL NETWORK; BRAIN HEMORRHAGE; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DISCRIMINATION LEARNING; FEMALE; GENERAL HOSPITAL; HUMAN; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MALE; MORTALITY; PERFORMANCE; PREDICTION; PRIORITY JOURNAL; RANDOM FOREST; RECEIVER OPERATING CHARACTERISTIC; RETROSPECTIVE STUDY; SENSITIVITY AND SPECIFICITY; STROKE; SUPPORT VECTOR MACHINE;

AGED; ALGORITHMS; ARTIFICIAL INTELLIGENCE; CEREBRAL HEMORRHAGE; DECISION SUPPORT SYSTEMS, CLINICAL; FEMALE; HUMANS; MALE; MIDDLE AGED; MORTALITY; PREDICTIVE VALUE OF TESTS; PROGNOSIS; RETROSPECTIVE STUDIES; RISK ASSESSMENT; SENSITIVITY AND SPECIFICITY;

EID: 77953833279     PISSN: 13515101     EISSN: 14681331     Source Type: Journal    
DOI: 10.1111/j.1468-1331.2010.02955.x     Document Type: Article

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