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Survival Analysis

Survival Analysis is a branch of statistics focused on the study of time-to-event data, usually called survival times. This type of data appears in a wide range of applications such as failure times in mechanical systems, death times of patients in a clinical trial or duration of unemployment in a population. One of the main objectives of Survival Analysis is the estimation of the so-called survival function and the hazard function. If a random variable has density function $f$ and cumulative distribution function $F$, then its survival function $S$ is $1-F$, and its hazard $λ$ is $f/S$.

Source: Gaussian Processes for Survival Analysis

Image: Kvamme et al.

Papers

Showing 321330 of 472 papers

TitleStatusHype
DAGSurv: Directed Acyclic Graph Based Survival Analysis Using Deep Neural NetworksCode0
Survival-oriented embeddings for improving accessibility to complex data structures0
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation0
Predictive factors associated with survival rate of cervical cancer patients in Brunei Darussalam0
Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards0
Metaparametric Neural Networks for Survival Analysis0
Energy-based survival modelling using harmoniumsCode0
Simpler Calibration for Survival Analysis0
Assumption-Free Survival Analysis Under Local Smoothness Prior0
Towards simple time-to-event modeling: optimizing neural networks via rank regression0
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