Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma

American Journal of Gastroenterology

Volumn 108, Issue 11, 2013, Pages 1723-1730

  Singal, Amit G ^a,b,c   Mukherjee, Ashin ^d   Joseph Elmunzer, B ^d   Higgins, Peter D R ^d   Lok, Anna S ^d   Zhu, Ji ^d   Marrero, Jorge A ^a   Waljee, Akbar K ^d,e  

^a UNIVERSITY OF TEXAS SOUTHWESTERN MEDICAL CENTER   (United States)

^b UNIVERSITY OF TEXAS SOUTHWESTERN MEDICAL CENTER   (United States)

^c UNIVERSITY OF TEXAS SOUTHWESTERN MEDICAL CENTER   (United States)

^d UNIVERSITY OF MICHIGAN   (United States)

^e VETERANS AFFAIRS MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANTIVIRUS AGENT;

ADULT; AGED; ALGORITHM; ARTICLE; CANCER CLASSIFICATION; CANCER GROWTH; CANCER RISK; CANCER STATISTICS; COHORT ANALYSIS; CONTROLLED STUDY; DEATH; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; FEMALE; FOLLOW UP; HEPATITIS C; HUMAN; HUMAN TISSUE; INDEPENDENT VARIABLE; LIVER CELL CARCINOMA; LIVER CIRRHOSIS; LIVER DEVELOPMENT; LIVER TRANSPLANTATION; LONG TERM CARE; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; PREDICTION; PRIORITY JOURNAL; PROSPECTIVE STUDY; RECEIVER OPERATING CHARACTERISTIC; REGRESSION ANALYSIS; UNITED STATES; UNIVERSITY HOSPITAL; VALIDATION STUDY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CARCINOMA, HEPATOCELLULAR; FEMALE; HEPATITIS B, CHRONIC; HEPATITIS C, CHRONIC; HUMANS; LIVER CIRRHOSIS; LIVER NEOPLASMS; LIVER TRANSPLANTATION; MALE; MIDDLE AGED; MODELS, THEORETICAL; REGRESSION ANALYSIS; RISK FACTORS;

EID: 84887246574     PISSN: 00029270     EISSN: 15720241     Source Type: Journal    
DOI: 10.1038/ajg.2013.332     Document Type: Article

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