Support vector methods for survival analysis: A comparison between ranking and regression approaches

Artificial Intelligence in Medicine

Volumn 53, Issue 2, 2011, Pages 107-118

  Van Belle, Vanya ^a   Pelckmans, Kristiaan ^b   Van Huffel, Sabine ^a   Suykens, Johan A K ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b UPPSALA UNIVERSITY   (Sweden)

Author keywords

Cancer prognosis; Concordance index; Support vector machines; Survival analysis

Indexed keywords

CANCER PROGNOSIS; CENSORED OBSERVATIONS; CLASSICAL APPROACH; CLINICAL DATA; CONCORDANCE INDEX; CONVEX OPTIMIZATION TECHNIQUES; DATA SETS; EMPIRICAL EVIDENCE; HAZARD RATIO; HIGH DIMENSIONAL DATA; HIGH-DIMENSIONAL; INEQUALITY CONSTRAINT; MACHINE-LEARNING; NEW MODEL; PROPORTIONAL HAZARDS MODEL; RANKING PROBLEMS; STATISTICAL MODELS; STRUCTURAL RISK MINIMIZATION; SUPPORT VECTOR; SUPPORT VECTOR METHOD; SURVIVAL ANALYSIS; SURVIVAL DATA; SURVIVAL MODEL; TRANSFORMATION MODEL;

BIOINFORMATICS; CLUSTERING ALGORITHMS; CONSTRAINT THEORY; CONVEX OPTIMIZATION; STATISTICAL TESTS; SUPPORT VECTOR MACHINES;

REGRESSION ANALYSIS;

ANALYTIC METHOD; ARTICLE; CONCORDANCE INDEX; KERNEL METHOD; MATHEMATICAL COMPUTING; MEDICAL LITERATURE; PRIORITY JOURNAL; PROGNOSIS; SUPPORT VECTOR MACHINE; SURVIVAL;

ARTIFICIAL INTELLIGENCE; BREAST NEOPLASMS; DATABASES, FACTUAL; FEMALE; HUMANS; LYMPHOMA, LARGE B-CELL, DIFFUSE; MALE; PROGNOSIS; PROPORTIONAL HAZARDS MODELS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES; SURVIVAL ANALYSIS;

EID: 80052431188     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2011.06.006     Document Type: Article

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