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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 441450 of 472 papers

TitleStatusHype
Overcoming Dependent Censoring in the Evaluation of Survival ModelsCode0
Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modellingCode0
A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular InformationCode0
Integrated Machine Learning and Survival Analysis Modeling for Enhanced Chronic Kidney Disease Risk StratificationCode0
Simultaneous Prediction Intervals for Patient-Specific Survival CurvesCode0
A Scalable Discrete-Time Survival Model for Neural NetworksCode0
Interpretable Machine Learning for Survival AnalysisCode0
Interpretable Non-linear Survival Analysis with Evolutionary Symbolic RegressionCode0
Variational Learning of Individual Survival DistributionsCode0
A Recurrent Neural Network Survival Model: Predicting Web User Return TimeCode0
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