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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 461470 of 472 papers

TitleStatusHype
Learning Genomic Representations to Predict Clinical Outcomes in CancerCode0
Predicting Survival Time of Ball Bearings in the Presence of CensoringCode0
SurvUnc: A Meta-Model Based Uncertainty Quantification Framework for Survival AnalysisCode0
Learning to rank for censored survival dataCode0
Leveraging Deep Representations of Radiology Reports in Survival Analysis for Predicting Heart Failure Patient MortalityCode0
Adaptive Transformer Modelling of Density Function for Nonparametric Survival AnalysisCode0
Transformer-based Time-to-Event Prediction for Chronic Kidney Disease DeteriorationCode0
Computing the Hazard Ratios Associated with Explanatory Variables Using Machine Learning Models of Survival DataCode0
TripleSurv: Triplet Time-adaptive Coordinate Loss for Survival AnalysisCode0
Targeted Estimation of Heterogeneous Treatment Effect in Observational Survival AnalysisCode0
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