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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 381390 of 472 papers

TitleStatusHype
Novel Radiomic Feature for Survival Prediction of Lung Cancer Patients using Low-Dose CBCT Images0
Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing RisksCode1
Survival Cluster AnalysisCode1
Estimation of conditional mixture Weibull distribution with right-censored data using neural network for time-to-event analysis0
Uncovering life-course patterns with causal discovery and survival analysis0
Multi-Task Deep Learning: Simultaneous Segmentation and Survival Analysis via Cox Proportional Hazards Regression0
An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial: A report from the Children's Oncology Group0
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion ProcessesCode0
Topic Models with Survival Supervision: Archetypal Analysis and Neural Approaches0
The Brier Score under Administrative Censoring: Problems and SolutionsCode0
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