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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 91–100 of 472 papers

TitleStatusHype
SeqRisk: Transformer-augmented latent variable model for improved survival prediction with longitudinal data—0
Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational PathologyCode2
A Cost-Aware Approach to Adversarial Robustness in Neural Networks—0
Adaptive Transformer Modelling of Density Function for Nonparametric Survival AnalysisCode0
MENSA: A Multi-Event Network for Survival Analysis under Informative CensoringCode0
forester: A Tree-Based AutoML Tool in RCode2
Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression LabelsCode1
CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival AnalysisCode1
End-to-end Multi-source Visual Prompt Tuning for Survival Analysis in Whole Slide Images—0
Estimating Heterogenous Treatment Effects for Survival Data with Doubly Doubly Robust Estimator—0
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