Deep Survival : The Deep Cox Proportional Hazards Network

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Opportunities and obstacles for deep learning in biology and medicine (2017, background): “in turn developed a deep implementation of the Faraggi-Simon network that, in addition to outperforming Cox regression, was capable of comparing the risk between a given pair of treatments, thus potentially acting as recommender system [86].”
Machine Learning for Survival Analysis: A Survey (2017, background): “Several deep survival analysis approaches [Katzman et al. 2016; Ranganath et al. 2016] are also proposed to assist the physicians with their clinical decisions on the patients by estimating their risk of diseases and provide personalized treatment recommendations.”
DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks (2018, methodology): “More recently, (Katzman et al. 2016) and (Luck et al. 2017) have followed the same general approach, although using more sophisticated network architectures and loss functions.”
Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks (2020, methodology): “first proposed a deep fully connected network (DeepSurv) to represent the nonlinear risk function [17].”