Predicting prostate cancer recurrence via maximizing the concordance index
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Missing data imputation in breast cancer prognosis
BioMed'06 Proceedings of the 24th IASTED international conference on Biomedical engineering
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Artificial neural networks are a powerful tool for analyzing data sets where there are complicated nonlinear interactions between the measured inputs and the quantity to be predicted. We show that the results obtained when neural networks are applied to survival data depend critically on the treatment of censoring in the data. When the censoring is modeled correctly, neural networks are a robust model independent technique for the analysis of very large sets of survival data