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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

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Showing 451–472 of 472 papers

TitleStatusHype
WSISA: Making Survival Prediction From Whole Slide Histopathological Images—0
Natural mortality of Trachurus novaezelandiae and their size selection by purse seines off south-eastern Australia—0
Deep Learning for Patient-Specific Kidney Graft Survival AnalysisCode0
Contextual Explanation NetworksCode0
Personalized Survival Predictions for Cardiac Transplantation via Trees of Predictors—0
Playtime Measurement with Survival Analysis—0
Identification of Cancer Patient Subgroups via Smoothed Shortest Path Graph Kernel—0
An Efficient Training Algorithm for Kernel Survival Support Vector MachinesCode0
Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets—0
Gaussian Processes for Survival Analysis—0
Learning Genomic Representations to Predict Clinical Outcomes in CancerCode0
Deep Survival Analysis—0
Multi-Organ Cancer Classification and Survival Analysis—0
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural NetworkCode1
Survival analysis, the infinite Gaussian mixture model, FDG-PET and non-imaging data in the prediction of progression from mild cognitive impairment—0
Hazard function models to estimate mortality rates affecting fish populations with application to the sea mullet (Mugil cephalus) fishery on the Queensland coast (Australia)—0
SGD with Variance Reduction beyond Empirical Risk Minimization—0
Discrete Stochastic Models in Continuous Time for Ecology—0
Kernel Machines for Current Status Data—0
Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data—0
Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors—0
On Ranking in Survival Analysis: Bounds on the Concordance Index—0
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