Deep Survival Analysis - Proceedings of Machine Learning Research
proceedings.mlr.press › Ranganath16
In this paper, we introduce deep survival analysis, a hierarchical generative approach to survival analysis in the context of the EHR.
May 24, 2023 · We conduct a comprehensive systematic review of DL-based methods for time-to-event analysis, characterizing them according to both survival- and DL-related ...
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Feb 19, 2024 · In this paper we provide a comprehensive review of currently available DL-based survival methods, addressing theoretical dimensions, such as model class and NN ...
Feb 26, 2018 · We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient's ...
Feb 6, 2020 · Survival analysis is a collection of data analysis methods with the outcome variable of interest time to event.
DeepSurv has an advantage over traditional Cox regression because it does not require an a priori selection of covariates, but learns them adaptively. DeepSurv ...
Aug 23, 2024 · Survival analysis focuses on estimating and predicting the survival time or the survival rate at a specified time.
This paper proposes a very different approach to survival analysis: we construct and use a deep neural network that learns the distribution of first hitting ...
Oct 1, 2023 · We proposed an interpretable deep survival analysis model named CoxNAM. This model is based on the Cox proportion hazards model and uses neural additive model ...
The deep survival model outperformed traditional methodology by identifying time-varied risk trajectories for preeclampsia, providing insights for early and ...