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 176–200 of 472 papers

TitleStatusHype
Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs)Code0
Estimation of Time-to-Total Knee Replacement Surgery—0
The TruEnd-procedure: Treating trailing zero-valued balances in credit data—0
Interpretable Prediction and Feature Selection for Survival Analysis—0
Explainable Survival Analysis with Uncertainty using Convolution-Involved Vision Transformer—0
Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis—0
Efficient Training of Probabilistic Neural Networks for Survival AnalysisCode0
Analyzing Economic Convergence Across the Americas: A Survival Analysis Approach to GDP per Capita Trajectories—0
Shared Hardships Strengthen Bonds: Negative Shocks, Embeddedness and Employee Retention—0
On Training Survival Models with Scoring Rules—0
Dynamic Survival Analysis for Early Event Prediction—0
Interpretable Machine Learning for Survival AnalysisCode0
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 admission—0
A network-constrain Weibull AFT model for biomarkers discovery—0
Differentially Private Distributed InferenceCode0
Online Learning Approach for Survival Analysis—0
OPSurv: Orthogonal Polynomials Quadrature Algorithm for Survival Analysis—0
Explainable AI for survival analysis: a median-SHAP approach—0
Dynamical Survival Analysis with Controlled Latent StatesCode0
High-Dimensional False Discovery Rate Control for Dependent Variables—0
Optimal Sparse Survival TreesCode0
SCANIA Component X Dataset: A Real-World Multivariate Time Series Dataset for Predictive Maintenance—0
Survival Analysis of Young Triple-Negative Breast Cancer Patients—0
TripleSurv: Triplet Time-adaptive Coordinate Loss for Survival AnalysisCode0
Show:102550
← PrevPage 8 of 19Next →

No leaderboard results yet.