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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 141150 of 472 papers

TitleStatusHype
Federated Survival ForestsCode0
Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival DataCode0
Binacox: automatic cut-point detection in high-dimensional Cox model with applications in geneticsCode0
Diffsurv: Differentiable sorting for censored time-to-event dataCode0
Flexible Group Fairness Metrics for Survival AnalysisCode0
BoXHED: Boosted eXact Hazard Estimator with Dynamic covariatesCode0
Distributionally Robust Survival Analysis: A Novel Fairness Loss Without DemographicsCode0
Exploring the Wasserstein metric for survival analysisCode0
DNAMite: Interpretable Calibrated Survival Analysis with Discretized Additive ModelsCode0
ALBRT: Cellular Composition Prediction in Routine Histology ImagesCode0
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