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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 421–430 of 472 papers

TitleStatusHype
Feature Selection for Survival Analysis with Competing Risks using Deep LearningCode0
An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes—0
Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risksCode0
SAFE: A Neural Survival Analysis Model for Fraud Early DetectionCode0
Deep Recurrent Survival AnalysisCode0
Your Actions or Your Associates? Predicting Certification and Dropout in MOOCs with Behavioral and Social Features—0
Image-based Survival Analysis for Lung Cancer Patients using CNNs—0
Binacox: automatic cut-point detection in high-dimensional Cox model with applications in geneticsCode0
Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework—0
Siamese Survival Analysis with Competing Risks—0
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