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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 261270 of 472 papers

TitleStatusHype
TV-SurvCaus: Dynamic Representation Balancing for Causal Survival Analysis0
Uncovering life-course patterns with causal discovery and survival analysis0
Une comparaison des algorithmes d'apprentissage pour la survie avec données manquantes0
Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence0
User Engagement in Mobile Health Applications0
Using ontology embeddings for structural inductive bias in gene expression data analysis0
Variable selection for nonlinear Cox regression model via deep learning0
Variable Selection with Random Survival Forest and Bayesian Additive Regression Tree for Survival Data0
Variational Bayes survival analysis for unemployment modelling0
Vision Transformers with Autoencoders and Explainable AI for Cancer Patient Risk Stratification Using Whole Slide Imaging0
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