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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 231240 of 472 papers

TitleStatusHype
Deep Clustering Survival Machines with Interpretable Expert DistributionsCode0
Maximum Mean Discrepancy Kernels for Predictive and Prognostic Modeling of Whole Slide ImagesCode0
Case-Base Neural Networks: survival analysis with time-varying, higher-order interactionsCode0
Novelty Detection in Network Traffic: Using Survival Analysis for Feature Identification0
Hospital transfer risk prediction for COVID-19 patients from a medicalized hotel based on Diffusion GraphSAGE0
Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithm0
AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide ImagesCode1
Examining marginal properness in the external validation of survival models with squared and logarithmic lossesCode0
Multi-Source Survival Domain AdaptationCode0
Distributionally Robust Survival Analysis: A Novel Fairness Loss Without DemographicsCode0
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