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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 431440 of 472 papers

TitleStatusHype
HistoKernel: Whole Slide Image Level Maximum Mean Discrepancy Kernels for Pan-Cancer Predictive ModellingCode0
Context Matters: Query-aware Dynamic Long Sequence Modeling of Gigapixel ImagesCode0
VGAT: A Cancer Survival Analysis Framework Transitioning from Generative Visual Question Answering to Genomic ReconstructionCode0
Semi-Supervised Variational Autoencoder for Survival PredictionCode0
A Study on Survival Analysis Methods Using Neural Network to Prevent CancersCode0
ICTSurF: Implicit Continuous-Time Survival Functions with Neural NetworksCode0
Using Geographic Location-based Public Health Features in Survival AnalysisCode0
The structure of online social networks modulates the rate of lexical changeCode0
Conformalized Survival Distributions: A Generic Post-Process to Increase CalibrationCode0
Learning Survival Distribution with Implicit Survival FunctionCode0
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