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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 401–410 of 472 papers

TitleStatusHype
Gradient Boosting Survival Tree with Applications in Credit ScoringCode0
DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo ValuesCode0
Time-to-Event Prediction with Neural Networks and Cox Regression—0
CNN-based Survival Model for Pancreatic Ductal Adenocarcinoma in Medical Imaging—0
From Non-Paying to Premium: Predicting User Conversion in Video Games with Ensemble Learning—0
Simultaneous Prediction Intervals for Patient-Specific Survival CurvesCode0
A unified construction for series representations and finite approximations of completely random measures—0
Unsupervised Machine Learning for the Discovery of Latent Disease Clusters and Patient Subgroups Using Electronic Health Records—0
Nonlinear Semi-Parametric Models for Survival AnalysisCode0
Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency RatesCode0
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