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• W3123284220 abstract "We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner. Our approach does not require making strong assumptions of constant proportional hazards of the underlying survival distribution, as required by the Cox-proportional hazard model. By jointly learning deep nonlinear representations of the input covariates, we demonstrate the benefits of our approach when used to estimate survival risks through extensive experimentation on multiple real world datasets with different levels of censoring. We further demonstrate advantages of our model in the competing risks scenario. To the best of our knowledge, this is the first work involving fully parametric estimation of survival times with competing risks in the presence of censoring." @default.
• W3123284220 created "2021-02-01" @default.
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• W3123284220 date "2021-08-01" @default.
• W3123284220 modified "2023-10-11" @default.
• W3123284220 title "<i>Deep Survival Machines</i>: Fully Parametric Survival Regression and Representation Learning for Censored Data With Competing Risks" @default.
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• W3123284220 doi "https://doi.org/10.1109/jbhi.2021.3052441" @default.
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