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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 426–450 of 472 papers

TitleStatusHype
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
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
Predicting Session Length in Media Streaming—0
iSurvive: An Interpretable, Event-time Prediction Model for mHealth—0
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