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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 6170 of 472 papers

TitleStatusHype
Tackling Small Sample Survival Analysis via Transfer Learning: A Study of Colorectal Cancer PrognosisCode1
Cross-Modal Translation and Alignment for Survival AnalysisCode1
Deep Cox Mixtures for Survival RegressionCode1
Discrete-time Competing-Risks Regression with or without PenalizationCode1
A Closer Look at Mortality Risk Prediction from ElectrocardiogramsCode1
CustOmics: A versatile deep-learning based strategy for multi-omics integrationCode1
DeepHazard: neural network for time-varying risksCode1
A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data0
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss0
4D VQ-GAN: Synthesising Medical Scans at Any Time Point for Personalised Disease Progression Modelling of Idiopathic Pulmonary Fibrosis0
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