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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 8190 of 472 papers

TitleStatusHype
A Scalable Discrete-Time Survival Model for Neural NetworksCode0
Adaptive Prototype Learning for Multimodal Cancer Survival AnalysisCode0
A Recurrent Neural Network Survival Model: Predicting Web User Return TimeCode0
Dynamic Survival Analysis for non-Markovian Epidemic ModelsCode0
AdaMHF: Adaptive Multimodal Hierarchical Fusion for Survival PredictionCode0
A novel gradient-based method for decision trees optimizing arbitrary differential loss functionsCode0
Dynamical Survival Analysis with Controlled Latent StatesCode0
Doubly Robust Conformalized Survival Analysis with Right-Censored DataCode0
Dynamic Entity-Masked Graph Diffusion Model for histopathological image Representation LearningCode0
Federated Survival ForestsCode0
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