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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 131140 of 472 papers

TitleStatusHype
Cancer Subtype Identification through Integrating Inter and Intra Dataset Relationships in Multi-Omics DataCode0
Calibration Error for Heterogeneous Treatment EffectsCode0
A Multi-Modal Deep Learning Framework for Pan-Cancer PrognosisCode0
BoXHED: Boosted eXact Hazard Estimator with Dynamic covariatesCode0
Extending Cox Proportional Hazards Model with Symbolic Non-Linear Log-Risk Functions for Survival AnalysisCode0
Fairness in Survival Analysis with Distributionally Robust OptimizationCode0
Gene-MOE: A sparsely gated prognosis and classification framework exploiting pan-cancer genomic informationCode0
BoXHED2.0: Scalable boosting of dynamic survival analysisCode0
Binacox: automatic cut-point detection in high-dimensional Cox model with applications in geneticsCode0
EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological ImagesCode0
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