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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 371380 of 472 papers

TitleStatusHype
MPVNN: Mutated Pathway Visible Neural Network Architecture for Interpretable Prediction of Cancer-specific Survival RiskCode0
Censor Dependent Variational InferenceCode0
Case-Base Neural Networks: survival analysis with time-varying, higher-order interactionsCode0
DeepHit: A Deep Learning Approach to Survival Analysis with Competing RisksCode0
Towards Flexible Time-to-event Modeling: Optimizing Neural Networks via Rank RegressionCode0
EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological ImagesCode0
TE-SSL: Time and Event-aware Self Supervised Learning for Alzheimer's Disease Progression AnalysisCode0
Cancer Subtype Identification through Integrating Inter and Intra Dataset Relationships in Multi-Omics DataCode0
Survival Analysis Using a 5-Step Stratified Testing and Amalgamation Routine in Randomized Clinical TrialsCode0
Calibration Error for Heterogeneous Treatment EffectsCode0
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