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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 451460 of 472 papers

TitleStatusHype
SurvReLU: Inherently Interpretable Survival Analysis via Deep ReLU NetworksCode0
Conformalized Survival AnalysisCode0
SurvTimeSurvival: Survival Analysis On The Patient With Multiple Visits/RecordsCode0
Inverse-Weighted Survival GamesCode0
A Deep Learning Approach for Overall Survival Prediction in Lung Cancer with Missing ValuesCode0
Efficient Training of Probabilistic Neural Networks for Survival AnalysisCode0
Structured Learning in Time-dependent Cox ModelsCode0
Knowledge-driven Subspace Fusion and Gradient Coordination for Multi-modal LearningCode0
Conditioning on Time is All You Need for Synthetic Survival Data GenerationCode0
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion ProcessesCode0
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