Machine learning can improve prediction of severity in acute pancreatitis using admission values of APACHE II score and C-reactive protein

Pancreatology

Volumn 6, Issue 1-2, 2006, Pages 123-131

  Pearce, Callum B ^a   Gunn, Steve R ^b   Ahmed, Adil ^a   Johnson, Colin D ^b  

^a SOUTHAMPTON GENERAL HOSPITAL   (United Kingdom)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

Acute pancreatitis; APACHE; Machine learning; Prediction; Severity

Indexed keywords

C REACTIVE PROTEIN; CREATININE;

ACCURACY; ACUTE PANCREATITIS; ADOLESCENT; ADULT; AGE; AGED; ALGORITHM; APACHE; AREA UNDER THE CURVE; ARTERIAL PH; ARTICLE; BLOOD EXAMINATION; BOOTSTRAPPING; BREATHING RATE; CLINICAL ASSESSMENT TOOL; COMPLICATION; COMPUTER; CREATININE BLOOD LEVEL; DATA ANALYSIS; DISEASE SEVERITY; ETIOLOGY; FEMALE; GLASGOW COMA SCALE; HOSPITAL ADMISSION; HUMAN; KERNEL LOGISTIC REGRESSION MODEL; LEUKOCYTE COUNT; LOGISTIC REGRESSION ANALYSIS; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; OXYGEN TENSION; PHYSICAL EXAMINATION; PREDICTION; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; STATISTICAL MODEL;

EID: 33646148476     PISSN: 14243903     EISSN: 14243911     Source Type: Journal    
DOI: 10.1159/000090032     Document Type: Article

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