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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 4150 of 472 papers

TitleStatusHype
AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide ImagesCode1
CustOmics: A versatile deep-learning based strategy for multi-omics integrationCode1
Survival Mixture Density NetworksCode1
A Comparative Study for Time-to-Event Analysis and Survival Prediction for Heart Failure Condition using Machine Learning TechniquesCode1
Censored Quantile Regression Neural Networks for Distribution-Free Survival AnalysisCode1
Hierarchical Bayesian Modelling for Knowledge Transfer Across Engineering Fleets via Multitask LearningCode1
PyDTS: A Python Package for Discrete-Time Survival (Regularized) Regression with Competing RisksCode1
Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical DataCode1
Survival Analysis Algorithms based on Decision Trees with Weighted Log-rank CriteriaCode1
SurvTRACE: Transformers for Survival Analysis with Competing EventsCode1
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