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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 76–100 of 472 papers

TitleStatusHype
A Statistical Learning Take on the Concordance Index for Survival Analysis—0
A State Transition Model for Mobile Notifications via Survival Analysis—0
Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction—0
Differentially Private Regression for Discrete-Time Survival Analysis—0
Assumption-Free Survival Analysis Under Local Smoothness Prior—0
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis—0
DeepWait: Pedestrian Wait Time Estimation in Mixed Traffic Conditions Using Deep Survival Analysis—0
DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Contrast-Enhanced CT Imaging—0
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
Deep Learning for Cancer Prognosis Prediction Using Portrait Photos by StyleGAN Embedding—0
Deep Learning Approach for Predicting 30 Day Readmissions after Coronary Artery Bypass Graft Surgery—0
Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithm—0
DeepMMSA: A Novel Multimodal Deep Learning Method for Non-small Cell Lung Cancer Survival Analysis—0
Deep Survival Analysis—0
Discrete Stochastic Models in Continuous Time for Ecology—0
Composite Survival Analysis: Learning with Auxiliary Aggregated Baselines and Survival Scores—0
Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework—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
An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes—0
Can-SAVE: Mass Cancer Risk Prediction via Survival Analysis Variables and EHR—0
Combining multi-site Magnetic Resonance Imaging with machine learning predicts survival in paediatric brain tumours—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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