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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

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Showing 191–200 of 472 papers

TitleStatusHype
Deep Learning for Patient-Specific Kidney Graft Survival AnalysisCode0
Efficient Training of Probabilistic Neural Networks for Survival AnalysisCode0
Gradient Boosting Survival Tree with Applications in Credit ScoringCode0
Interpretable Machine Learning for Survival AnalysisCode0
MPVNN: Mutated Pathway Visible Neural Network Architecture for Interpretable Prediction of Cancer-specific Survival RiskCode0
Flexible Group Fairness Metrics for Survival AnalysisCode0
Deep Learning for Cancer Prognosis Prediction Using Portrait Photos by StyleGAN Embedding—0
Deep Learning Approach for Predicting 30 Day Readmissions after Coronary Artery Bypass Graft Surgery—0
A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data—0
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss—0
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