SOTAVerified

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 171180 of 472 papers

TitleStatusHype
Exploring the Wasserstein metric for survival analysisCode0
Neural Fine-Gray: Monotonic neural networks for competing risksCode0
A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional DataCode0
Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features RelateCode0
Extending Cox Proportional Hazards Model with Symbolic Non-Linear Log-Risk Functions for Survival AnalysisCode0
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measuresCode0
Deep Learning for Patient-Specific Kidney Graft Survival AnalysisCode0
Optimal Sparse Survival TreesCode0
Efficient Training of Probabilistic Neural Networks for Survival AnalysisCode0
Fairness in Survival Analysis with Distributionally Robust OptimizationCode0
Show:102550
← PrevPage 18 of 48Next →

No leaderboard results yet.