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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 341350 of 472 papers

TitleStatusHype
Long-Term Pipeline Failure Prediction Using Nonparametric Survival Analysis0
Modeling Long Sequences in Bladder Cancer Recurrence: A Comparative Evaluation of LSTM,Transformer,and Mamba0
Machine Learning for Survival Analysis: A Survey0
Deep Recurrent Survival AnalysisCode0
Deep Neural Networks for Survival Analysis Based on a Multi-Task FrameworkCode0
A novel gradient-based method for decision trees optimizing arbitrary differential loss functionsCode0
Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival DataCode0
Energy-based survival modelling using harmoniumsCode0
Maximum Mean Discrepancy Kernels for Predictive and Prognostic Modeling of Whole Slide ImagesCode0
Diffsurv: Differentiable sorting for censored time-to-event dataCode0
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