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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 221230 of 472 papers

TitleStatusHype
Penalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patients0
Discrete-time Competing-Risks Regression with or without PenalizationCode1
SurvivalGAN: Generating Time-to-Event Data for Survival AnalysisCode1
A Statistical Learning Take on the Concordance Index for Survival Analysis0
SurvLIMEpy: A Python package implementing SurvLIMECode1
Federated Survival ForestsCode0
Towards inferring network properties from epidemic data0
The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligence0
Heterogeneous Datasets for Federated Survival Analysis SimulationCode0
Adaptive Sampling for Weighted Log-Rank Survival Trees BoostingCode1
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