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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 426–450 of 472 papers

TitleStatusHype
Maximum Likelihood Estimation of Flexible Survival Densities with Importance Sampling—0
Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planning—0
Metaparametric Neural Networks for Survival Analysis—0
mlr3proba: An R Package for Machine Learning in Survival Analysis—0
Modeling 3D cardiac contraction and relaxation with point cloud deformation networks—0
Pricing Time-to-Event Contingent Cash Flows: A Discrete-Time Survival Analysis Approach—0
Modeling Time to Open of Emails with a Latent State for User Engagement Level—0
Mortality Analysis of Early COVID-19 Cases in the Philippines Based on Observed Demographic and Clinical Characteristics—0
Multi-modal Data Binding for Survival Analysis Modeling with Incomplete Data and Annotations—0
Multimodal Learning for Non-small Cell Lung Cancer Prognosis—0
Multi-Organ Cancer Classification and Survival Analysis—0
Multi-Scale User Behavior Network for Entire Space Multi-Task Learning—0
Multitask Boosting for Survival Analysis with Competing Risks—0
Multi-Task Deep Learning: Simultaneous Segmentation and Survival Analysis via Cox Proportional Hazards Regression—0
Natural mortality of Trachurus novaezelandiae and their size selection by purse seines off south-eastern Australia—0
Necessary and sufficient conditions for exact closures of epidemic equations on configuration model networks—0
Netboost: Boosting-supported network analysis improves high-dimensional omics prediction in acute myeloid leukemia and Huntington's disease—0
No-regret Learning in Repeated First-Price Auctions with Budget Constraints—0
Novel Radiomic Feature for Survival Prediction of Lung Cancer Patients using Low-Dose CBCT Images—0
Novelty Detection in Network Traffic: Using Survival Analysis for Feature Identification—0
Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review—0
Online Learning Approach for Survival Analysis—0
On Ranking in Survival Analysis: Bounds on the Concordance Index—0
Open-radiomics: A Collection of Standardized Datasets and a Technical Protocol for Reproducible Radiomics Machine Learning Pipelines—0
OPSurv: Orthogonal Polynomials Quadrature Algorithm for Survival Analysis—0
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