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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

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Showing 381–390 of 472 papers

TitleStatusHype
Survival Analysis Using a 5-Step Stratified Testing and Amalgamation Routine in Randomized Clinical TrialsCode0
An RNN-Survival Model to Decide Email Send Times—0
Combining multi-site Magnetic Resonance Imaging with machine learning predicts survival in paediatric brain tumours—0
Variational Learning of Individual Survival DistributionsCode0
Novel Radiomic Feature for Survival Prediction of Lung Cancer Patients using Low-Dose CBCT Images—0
Estimation of conditional mixture Weibull distribution with right-censored data using neural network for time-to-event analysis—0
Uncovering life-course patterns with causal discovery and survival analysis—0
Multi-Task Deep Learning: Simultaneous Segmentation and Survival Analysis via Cox Proportional Hazards Regression—0
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 Group—0
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion ProcessesCode0
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