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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
Learning Survival Distribution with Implicit Survival FunctionCode0
Adaptive Transformer Modelling of Density Function for Nonparametric Survival AnalysisCode0
Doubly Robust Conformalized Survival Analysis with Right-Censored DataCode0
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
Distributionally Robust Survival Analysis: A Novel Fairness Loss Without DemographicsCode0
Dynamical Survival Analysis with Controlled Latent StatesCode0
A Scalable Discrete-Time Survival Model for Neural NetworksCode0
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