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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 191–200 of 472 papers

TitleStatusHype
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer—0
Graph Domain Adaptation with Dual-branch Encoder and Two-level Alignment for Whole Slide Image-based Survival Prediction—0
An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes—0
FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysis—0
High-Dimensional False Discovery Rate Control for Dependent Variables—0
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications—0
ICBM community cancer registry analysis: a focus on Non-Hodgkin Lymphoma cases in missileers—0
KL-divergence Based Deep Learning for Discrete Time Model—0
Differentially Private Regression for Discrete-Time Survival Analysis—0
Development of digitally obtainable 10-year risk scores for depression and anxiety in the general population—0
Show:102550
← PrevPage 20 of 48Next →

No leaderboard results yet.