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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 171180 of 472 papers

TitleStatusHype
Maximum Likelihood Estimation of Flexible Survival Densities with Importance Sampling0
Higher Mediterranean diet score is associated with longer time between relapses in Australian females with multiple sclerosis0
DySurv: dynamic deep learning model for survival analysis with conditional variational inference0
Interpretable Survival Analysis for Heart Failure Risk Prediction0
Improving Event Time Prediction by Learning to Partition the Event Time Space0
Sensitivity of Survival Analysis MetricsCode1
A Study on Survival Analysis Methods Using Neural Network to Prevent CancersCode0
Can-SAVE: Mass Cancer Risk Prediction via Survival Analysis Variables and EHR0
A survey of Transformer applications for histopathological image analysis: New developments and future directionsCode0
Predicting environment effects on breast cancer by implementing machine learning0
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