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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 81–90 of 472 papers

TitleStatusHype
An RNN-Survival Model to Decide Email Send Times—0
Advancing clinical trial outcomes using deep learning and predictive modelling: bridging precision medicine and patient-centered care—0
A Differentially Private Kaplan-Meier Estimator for Privacy-Preserving Survival Analysis—0
An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes—0
Deep Convolutional Neural Networks for Imaging Data Based Survival Analysis of Rectal Cancer—0
Deep End-to-End Survival Analysis with Temporal Consistency—0
Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithm—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
Unsupervised Machine Learning for the Discovery of Latent Disease Clusters and Patient Subgroups Using Electronic Health Records—0
CNN-based Survival Model for Pancreatic Ductal Adenocarcinoma in Medical Imaging—0
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