Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks

Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks

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-propo...

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Bibliographic Details
Main Authors: Dubrawski, A. (Author), Li, X. (Author), Nagpal, C. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
article
bioinformatics
censored regression
Competing risks
controlled study
Cox proportional hazard model
deep learning
Deep learning
graphical models
Hazards
human
Humans
mixture of experts
Models, Statistical
New approaches
Parameter estimation
Parametric estimation
prediction
proportional hazards model
Proportional Hazards Models
Real-world datasets
Relative risks
remission
risk
Risk
risk assessment
risk factor
Risk perception
statistical model
survival analysis
Survival analysis
Survival Analysis
Survival distributions
survival time
Time to events
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