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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 301–310 of 472 papers

TitleStatusHype
An Approach for Clustering Subjects According to Similarities in Cell Distributions within Biopsies—0
A network-constrain Weibull AFT model for biomarkers discovery—0
An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial: A report from the Children's Oncology Group—0
An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes—0
An RNN-Survival Model to Decide Email Send Times—0
Assumption-Free Survival Analysis Under Local Smoothness Prior—0
A State Transition Model for Mobile Notifications via Survival Analysis—0
A Statistical Learning Take on the Concordance Index for Survival Analysis—0
Attention-Based Synthetic Data Generation for Calibration-Enhanced Survival Analysis: A Case Study for Chronic Kidney Disease Using Electronic Health Records—0
A unified construction for series representations and finite approximations of completely random measures—0
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