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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 5160 of 472 papers

TitleStatusHype
Locally Sparse Neural Networks for Tabular Biomedical DataCode1
A Deep Variational Approach to Clustering Survival DataCode1
CDS -- Causal Inference with Deep Survival Model and Time-varying CovariatesCode1
Deep Cox Mixtures for Survival RegressionCode1
X-CAL: Explicit Calibration for Survival AnalysisCode1
Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue SystemsCode1
SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation NetworksCode1
DeepHazard: neural network for time-varying risksCode1
Deep Kernel Survival Analysis and Subject-Specific Survival Time Prediction IntervalsCode1
Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal CancerCode1
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