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 141150 of 472 papers

TitleStatusHype
Dynamic Survival Analysis for Early Event Prediction0
Interpretable Machine Learning for Survival AnalysisCode0
HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context InteractionCode2
Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival DataCode0
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission0
A network-constrain Weibull AFT model for biomarkers discovery0
Differentially Private Distributed InferenceCode0
Online Learning Approach for Survival Analysis0
OPSurv: Orthogonal Polynomials Quadrature Algorithm for Survival Analysis0
Explainable AI for survival analysis: a median-SHAP approach0
Show:102550
← PrevPage 15 of 48Next →

No leaderboard results yet.