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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 401–425 of 472 papers

TitleStatusHype
A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data—0
Modeling Time to Open of Emails with a Latent State for User Engagement Level—0
Gradient Boosting Survival Tree with Applications in Credit ScoringCode0
DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo ValuesCode0
Time-to-Event Prediction with Neural Networks and Cox Regression—0
From Non-Paying to Premium: Predicting User Conversion in Video Games with Ensemble Learning—0
Simultaneous Prediction Intervals for Patient-Specific Survival CurvesCode0
CNN-based Survival Model for Pancreatic Ductal Adenocarcinoma in Medical Imaging—0
A unified construction for series representations and finite approximations of completely random measures—0
Unsupervised Machine Learning for the Discovery of Latent Disease Clusters and Patient Subgroups Using Electronic Health Records—0
Nonlinear Semi-Parametric Models for Survival AnalysisCode0
Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency RatesCode0
Deep Landscape Forecasting for Real-time Bidding AdvertisingCode0
Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data—0
A Deep Learning Approach for Dynamic Survival Analysis with Competing Risks—0
DeepWait: Pedestrian Wait Time Estimation in Mixed Traffic Conditions Using Deep Survival Analysis—0
Deep Convolutional Neural Networks for Imaging Data Based Survival Analysis of Rectal Cancer—0
Multivariate Arrival Times with Recurrent Neural Networks for Personalized Demand ForecastingCode0
Deep Learning Approach for Predicting 30 Day Readmissions after Coronary Artery Bypass Graft Surgery—0
Multitask Boosting for Survival Analysis with Competing Risks—0
A Deep Latent-Variable Model Application to Select Treatment Intensity in Survival Analysis—0
MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks—0
Feature Selection for Survival Analysis with Competing Risks using Deep LearningCode0
An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes—0
Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risksCode0
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