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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 261–270 of 472 papers

TitleStatusHype
KL-divergence Based Deep Learning for Discrete Time Model—0
Child Care Provider Survival Analysis—0
Multi-Scale User Behavior Network for Entire Space Multi-Task Learning—0
Likelihood-Free Dynamical Survival Analysis Applied to the COVID-19 Epidemic in Ohio—0
Open-radiomics: A Collection of Standardized Datasets and a Technical Protocol for Reproducible Radiomics Machine Learning Pipelines—0
Subtype-Former: a deep learning approach for cancer subtype discovery with multi-omics data—0
A Comparative Study for Time-to-Event Analysis and Survival Prediction for Heart Failure Condition using Machine Learning TechniquesCode1
FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysis—0
Pseudo value-based Deep Neural Networks for Multi-state Survival Analysis—0
A State Transition Model for Mobile Notifications via Survival Analysis—0
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