A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer

Artificial Intelligence in Medicine

Volumn 28, Issue 1, 2003, Pages 1-25

  Lisboa, P J G ^a   Wong, H ^a   Harris, P ^a   Swindell, R ^b  

^a LIVERPOOL JOHN MOORES UNIVERSITY   (United Kingdom)

^b CHRISTIE HOSPITAL   (United Kingdom)

Author keywords

Artificial neural networks; Automatic Relevance Determination (ARD); Censorship; Cohort study; Nottingham Prognostic Index (NPI); Proportional hazards

Indexed keywords

HEALTH HAZARDS; MATHEMATICAL MODELS; SURGERY; TUMORS;

CLINICAL STAGING;

FEEDFORWARD NEURAL NETWORKS;

ARTICLE; BAYES THEOREM; BREAST CANCER; BREAST SURGERY; CANCER STAGING; CLINICAL PROTOCOL; COHORT ANALYSIS; CONTROLLED STUDY; DATA ANALYSIS; FEMALE; FOLLOW UP; HEALTH HAZARD; HIGH RISK POPULATION; HOSPITAL; HUMAN; INFORMATION PROCESSING; MAJOR CLINICAL STUDY; MEDICAL RECORD; METHODOLOGY; MODEL; NERVE CELL NETWORK; PRIORITY JOURNAL; PROGNOSIS;

EID: 0038162240     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0933-3657(03)00033-2     Document Type: Article

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