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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 7180 of 472 papers

TitleStatusHype
Adaptive Transformer Modelling of Density Function for Nonparametric Survival AnalysisCode0
Distributionally Robust Survival Analysis: A Novel Fairness Loss Without DemographicsCode0
DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo ValuesCode0
A survey of Transformer applications for histopathological image analysis: New developments and future directionsCode0
A Study on Survival Analysis Methods Using Neural Network to Prevent CancersCode0
A General Machine Learning Framework for Survival AnalysisCode0
A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular InformationCode0
Diffsurv: Differentiable sorting for censored time-to-event dataCode0
Doubly Robust Conformalized Survival Analysis with Right-Censored DataCode0
A Scalable Discrete-Time Survival Model for Neural NetworksCode0
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