HIGHLIGHTS
SUMMARY
Artificial neural_networks (ANNs) and other ML techniques have been used in healthcare for clinical diagnosis, prediction, and to support decision making, e_g, in the domains of cancer and cardiology. Two decades ago, Ripley B. and Ripley R. published an overview that identifies the most appropriate survival neural_networks (SNNs) for medical applications. The authors present the methodological approaches of neural_networks for survival analysis in chronological order. The simplest type of a neural_network is a FFANN where the information moves in only one direction-forward: from the input units to the hidden units (if any) and . . .
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