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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 311320 of 472 papers

TitleStatusHype
Dynamic Survival Analysis for non-Markovian Epidemic ModelsCode0
Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy0
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis0
Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data0
MPVNN: Mutated Pathway Visible Neural Network Architecture for Interpretable Prediction of Cancer-specific Survival RiskCode0
A Multi-modal Fusion Framework Based on Multi-task Correlation Learning for Cancer Prognosis Prediction0
Pricing Time-to-Event Contingent Cash Flows: A Discrete-Time Survival Analysis Approach0
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measuresCode0
Deep Extended Hazard Models for Survival Analysis0
Inverse-Weighted Survival GamesCode0
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