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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 321330 of 472 papers

TitleStatusHype
A Deep Variational Approach to Clustering Survival DataCode1
Mortality Analysis of Early COVID-19 Cases in the Philippines Based on Observed Demographic and Clinical Characteristics0
Leveraging Deep Representations of Radiology Reports in Survival Analysis for Predicting Heart Failure Patient MortalityCode0
Development of digitally obtainable 10-year risk scores for depression and anxiety in the general population0
SurvNAM: The machine learning survival model explanation0
The structure of online social networks modulates the rate of lexical changeCode0
BoXHED2.0: Scalable boosting of dynamic survival analysisCode0
BERTSurv: BERT-Based Survival Models for Predicting Outcomes of Trauma Patients0
Conformalized Survival AnalysisCode0
Predicting Kidney Transplant Survival using Multiple Feature Representations for HLAs0
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