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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 251260 of 472 papers

TitleStatusHype
EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological ImagesCode0
Factor-Augmented Regularized Model for Hazard Regression0
CustOmics: A versatile deep-learning based strategy for multi-omics integrationCode1
Temporal Pattern Mining for Analysis of Longitudinal Clinical Data: Identifying Risk Factors for Alzheimer's Disease0
Copula Entropy based Variable Selection for Survival AnalysisCode0
Temporal Label Smoothing for Early Event PredictionCode0
Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review0
Survival Mixture Density NetworksCode1
FastCPH: Efficient Survival Analysis for Neural NetworksCode2
Necessary and sufficient conditions for exact closures of epidemic equations on configuration model networks0
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