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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 6170 of 472 papers

TitleStatusHype
SurvAttack: Black-Box Attack On Survival Models through Ontology-Informed EHR Perturbation0
From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba0
Dynamic Entity-Masked Graph Diffusion Model for histopathological image Representation LearningCode0
Doubly Robust Conformalized Survival Analysis with Right-Censored DataCode0
SurvBETA: Ensemble-Based Survival Models Using Beran Estimators and Several Attention MechanismsCode0
Advancing clinical trial outcomes using deep learning and predictive modelling: bridging precision medicine and patient-centered care0
A Differentially Private Kaplan-Meier Estimator for Privacy-Preserving Survival Analysis0
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss0
EsurvFusion: An evidential multimodal survival fusion model based on Gaussian random fuzzy numbers0
2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image ClassificationCode2
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