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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 111120 of 472 papers

TitleStatusHype
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measuresCode0
CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learningCode0
A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular InformationCode0
Deep Clustering Survival Machines with Interpretable Expert DistributionsCode0
Exploring the Wasserstein metric for survival analysisCode0
A General Machine Learning Framework for Survival AnalysisCode0
A Deep Learning Approach for Overall Survival Prediction in Lung Cancer with Missing ValuesCode0
EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological ImagesCode0
Extending Cox Proportional Hazards Model with Symbolic Non-Linear Log-Risk Functions for Survival AnalysisCode0
Dynamic Entity-Masked Graph Diffusion Model for histopathological image Representation LearningCode0
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