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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 201–225 of 472 papers

TitleStatusHype
Deep Extended Hazard Models for Survival Analysis—0
Deep End-to-End Survival Analysis with Temporal Consistency—0
A unified construction for series representations and finite approximations of completely random measures—0
Attention-Based Synthetic Data Generation for Calibration-Enhanced Survival Analysis: A Case Study for Chronic Kidney Disease Using Electronic Health Records—0
Deep Convolutional Neural Networks for Imaging Data Based Survival Analysis of Rectal Cancer—0
Deep conditional transformation models for survival analysis—0
Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers—0
A Statistical Learning Take on the Concordance Index for Survival Analysis—0
A State Transition Model for Mobile Notifications via Survival Analysis—0
A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study—0
Higher Mediterranean diet score is associated with longer time between relapses in Australian females with multiple sclerosis—0
High-Dimensional False Discovery Rate Control for Dependent Variables—0
Hazard Gradient Penalty for Survival Analysis—0
Hazard function models to estimate mortality rates affecting fish populations with application to the sea mullet (Mugil cephalus) fishery on the Queensland coast (Australia)—0
CoxSE: Exploring the Potential of Self-Explaining Neural Networks with Cox Proportional Hazards Model for Survival Analysis—0
GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization—0
Copula-Based Deep Survival Models for Dependent Censoring—0
Graph Domain Adaptation with Dual-branch Encoder and Two-level Alignment for Whole Slide Image-based Survival Prediction—0
Global Censored Quantile Random Forest—0
Generalized Bayesian Ensemble Survival Tree (GBEST) model—0
Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records—0
Continuous Risk Measures for Driving Support—0
Assumption-Free Survival Analysis Under Local Smoothness Prior—0
Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy—0
Gaussian Processes for Survival Analysis—0
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