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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
Deep Learning for Survival Analysis: A ReviewCode1
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural NetworkCode1
Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression LabelsCode1
Locally Sparse Neural Networks for Tabular Biomedical DataCode1
A Closer Look at Mortality Risk Prediction from ElectrocardiogramsCode1
Discrete-time Competing-Risks Regression with or without PenalizationCode1
PyDTS: A Python Package for Discrete-Time Survival (Regularized) Regression with Competing RisksCode1
Deep Learning for Patient-Specific Kidney Graft Survival AnalysisCode0
A kernel log-rank test of independence for right-censored dataCode0
Learning Survival Distribution with Implicit Survival FunctionCode0
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