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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 111–120 of 472 papers

TitleStatusHype
A network-constrain Weibull AFT model for biomarkers discovery—0
CLSA: Contrastive Learning-based Survival Analysis for Popularity Prediction in MEC Networks—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
Deep conditional transformation models for survival analysis—0
Deep Convolutional Neural Networks for Imaging Data Based Survival Analysis of Rectal Cancer—0
Actionable Recourse via GANs for Mobile Health—0
Clinical Characteristics and Laboratory Biomarkers in ICU-admitted Septic Patients with and without Bacteremia—0
Deep End-to-End Survival Analysis with Temporal Consistency—0
An Approach for Clustering Subjects According to Similarities in Cell Distributions within Biopsies—0
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