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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 81–90 of 472 papers

TitleStatusHype
SurvUnc: A Meta-Model Based Uncertainty Quantification Framework for Survival AnalysisCode0
Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data—0
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification—0
Too Sick for Working, or Sick of Working? Impact of Acute Health Shocks on Early Labour Market Exits—0
Learning Survival Distributions with the Asymmetric Laplace Distribution—0
STG: Spatiotemporal Graph Neural Network with Fusion and Spatiotemporal Decoupling Learning for Prognostic Prediction of Colorectal Cancer Liver Metastasis—0
Prediction of Delirium Risk in Mild Cognitive Impairment Using Time-Series data, Machine Learning and Comorbidity Patterns -- A Retrospective Study—0
TV-SurvCaus: Dynamic Representation Balancing for Causal Survival Analysis—0
Predictive Multiplicity in Survival Models: A Method for Quantifying Model Uncertainty in Predictive Maintenance Applications—0
Forecasting from Clinical Textual Time Series: Adaptations of the Encoder and Decoder Language Model Families—0
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