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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 401–450 of 472 papers

TitleStatusHype
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
CNN-based Survival Model for Pancreatic Ductal Adenocarcinoma in Medical Imaging—0
From Non-Paying to Premium: Predicting User Conversion in Video Games with Ensemble Learning—0
Simultaneous Prediction Intervals for Patient-Specific Survival CurvesCode0
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
SAFE: A Neural Survival Analysis Model for Fraud Early DetectionCode0
Deep Recurrent Survival AnalysisCode0
Your Actions or Your Associates? Predicting Certification and Dropout in MOOCs with Behavioral and Social Features—0
Image-based Survival Analysis for Lung Cancer Patients using CNNs—0
Binacox: automatic cut-point detection in high-dimensional Cox model with applications in geneticsCode0
Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework—0
Siamese Survival Analysis with Competing Risks—0
A Recurrent Neural Network Survival Model: Predicting Web User Return TimeCode0
Learning to rank for censored survival dataCode0
GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settingsCode0
GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization—0
A Scalable Discrete-Time Survival Model for Neural NetworksCode0
Q-Map: Clinical Concept Mining from Clinical Documents—0
DeepHit: A Deep Learning Approach to Survival Analysis with Competing RisksCode0
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer—0
Adversarial Time-to-Event ModelingCode1
Causal Inference for Survival Analysis—0
Deep Neural Networks for Survival Analysis Based on a Multi-Task FrameworkCode0
Survival-Supervised Topic Modeling with Anchor Words: Characterizing Pancreatitis Outcomes—0
Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification—0
Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles—0
Statistical Inference for Data-adaptive Doubly Robust Estimators with Survival Outcomes—0
Differentially Private Regression for Discrete-Time Survival Analysis—0
Machine Learning for Survival Analysis: A Survey—0
iSurvive: An Interpretable, Event-time Prediction Model for mHealth—0
Predicting Session Length in Media Streaming—0
Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modellingCode0
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