Sepsis mortality prediction with the Quotient Basis Kernel

Artificial Intelligence in Medicine

Volumn 61, Issue 1, 2014, Pages 45-52

  Ribas Ripoll, Vicent J ^a   Vellido, Alfredo ^b   Romero, Enrique ^b   Ruiz Rodríguez, Juan Carlos ^c  

^a CENTRE DE RECERCA MATEMÀTICA   (Spain)

^b UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

^c HOSPITAL UNIVERSITARI VALL D HEBRON   (Spain)

Author keywords

Critical care; Kernels; Mortality prediction; Sepsis; Support vector machines

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DECISION SUPPORT SYSTEMS; DIAGNOSIS; INFERENCE ENGINES; INTENSIVE CARE UNITS; LINEAR ALGEBRA; PHYSIOLOGY; REGRESSION ANALYSIS; RISK ASSESSMENT; SUPPORT VECTOR MACHINES;

CLINICAL DECISION SUPPORT SYSTEMS; CRITICAL CARE; CRITICALLY-ILL PATIENTS; KERNELS; MULTINOMIAL DISTRIBUTIONS; SENSITIVITY AND SPECIFICITY; SEPSIS; SOFT-MARGIN SUPPORT VECTOR MACHINES;

FORECASTING;

ACCURACY; AGED; ARTICLE; CONTROLLED STUDY; CRITICALLY ILL PATIENT; FEMALE; HUMAN; INTENSIVE CARE; KERNEL METHOD; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MALE; MORTALITY; PREDICTION; PRIORITY JOURNAL; QUOTIENT BASIS KERNEL; RISK ASSESSMENT; SENSITIVITY AND SPECIFICITY; SEPSIS; SEQUENTIAL ORGAN FAILURE ASSESSMENT SCORE; SIMPLIFIED ACUTE PHYSIOLOGY SCORE; SUPPORT VECTOR MACHINE; VALIDATION PROCESS; ALGORITHM; ARTIFICIAL INTELLIGENCE; PROCEDURES; VALIDATION STUDY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; RISK ASSESSMENT; SENSITIVITY AND SPECIFICITY; SEPSIS;

EID: 84900533700     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2014.03.004     Document Type: Article

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