Single and multiple time-point prediction models in kidney transplant outcomes

Journal of Biomedical Informatics

Volumn 41, Issue 6, 2008, Pages 944-952

  Lin, Ray S ^a   Horn, Susan D ^b,c   Hurdle, John F ^c   Goldfarb Rumyantzev, Alexander S ^d  

^a STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b INSTITUTE FOR CLINICAL OUTCOMES RESEARCH   (United States)

^c UNIVERSITY OF UTAH SCHOOL OF MEDICINE   (United States)

^d HARVARD MEDICAL SCHOOL   (United States)

Author keywords

Artificial neural networks; Cox proportional hazards models; Graft survival; Kidney transplantation; Logistic regression; Recipient survival; Survival analysis

Indexed keywords

BACKPROPAGATION; CALIBRATION; DECISION SUPPORT SYSTEMS; ELECTRIC FAULT LOCATION; FORECASTING; GRAFTING (CHEMICAL); LOGISTICS; REGRESSION ANALYSIS;

ARTIFICIAL NEURAL NETWORKS; COX PROPORTIONAL HAZARDS MODELS; KIDNEY TRANSPLANTATION; LOGISTIC REGRESSION; RECIPIENT SURVIVAL; SURVIVAL ANALYSIS;

NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CALIBRATION; CLINICAL DECISION MAKING; DECISION SUPPORT SYSTEM; GRAFT SURVIVAL; HUMAN; KIDNEY GRAFT; KIDNEY TRANSPLANTATION; LOGISTIC REGRESSION ANALYSIS; NONLINEAR SYSTEM; OUTCOME ASSESSMENT; PRIORITY JOURNAL; PROPORTIONAL HAZARDS MODEL; RECIPIENT; SURVIVAL RATE;

HUMANS; KIDNEY TRANSPLANTATION; LOGISTIC MODELS; MODELS, THEORETICAL; PROPORTIONAL HAZARDS MODELS;

EID: 55549120208     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2008.03.005     Document Type: Article

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