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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

Showing 431440 of 472 papers

TitleStatusHype
Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework0
Siamese Survival Analysis with Competing Risks0
A Recurrent Neural Network Survival Model: Predicting Web User Return TimeCode0
Learning to rank for censored survival dataCode0
GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settingsCode0
GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization0
A Scalable Discrete-Time Survival Model for Neural NetworksCode0
Q-Map: Clinical Concept Mining from Clinical Documents0
DeepHit: A Deep Learning Approach to Survival Analysis with Competing RisksCode0
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer0
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