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Deep Survival: A Deep Cox Proportional Hazards Network | | Jared Katzman
; Uri Shaham
; Alexander Cloninger
; Jonathan Bates
; Tingting Jiang
; Yuval Kluger
; | | Date: |
3 Jun 2016 | | Abstract: | Previous research has shown that neural networks can model survival data in
situations in which some patients’ death times are unknown, e.g.
right-censored. However, neural networks have rarely been shown to outperform
their linear counterparts such as the Cox proportional hazards model. In this
paper, we run simulated experiments and use real survival data to build upon
the risk-regression architecture proposed by Faraggi and Simon. We demonstrate
that our model, DeepSurv, not only works as well as the standard linear Cox
proportional hazards model but actually outperforms it in predictive ability on
survival data with linear and nonlinear risk functions. We then show that the
neural network can also serve as a recommender system by including a
categorical variable representing a treatment group. This can be used to
provide personalized treatment recommendations based on an individual’s
calculated risk. We provide an open source Python module that implements these
methods in order to advance research on deep learning and survival analysis. | | Source: | arXiv, 1606.0931 | | Services: | Forum | Review | PDF | Favorites | |
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