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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 101–125 of 472 papers

TitleStatusHype
An RNN-Survival Model to Decide Email Send Times—0
Continuous and Discrete-Time Survival Prediction with Neural Networks—0
Composite Survival Analysis: Learning with Auxiliary Aggregated Baselines and Survival Scores—0
Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records—0
Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework—0
Copula-Based Deep Survival Models for Dependent Censoring—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
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
Assumption-Free Survival Analysis Under Local Smoothness Prior—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
Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers—0
A Statistical Learning Take on the Concordance Index for Survival Analysis—0
A network-constrain Weibull AFT model for biomarkers discovery—0
Deep Convolutional Neural Networks for Imaging Data Based Survival Analysis of Rectal Cancer—0
CLSA: Contrastive Learning-based Survival Analysis for Popularity Prediction in MEC Networks—0
Actionable Recourse via GANs for Mobile Health—0
Deep End-to-End Survival Analysis with Temporal Consistency—0
Deep Extended Hazard Models for Survival Analysis—0
Development of digitally obtainable 10-year risk scores for depression and anxiety in the general population—0
A unified construction for series representations and finite approximations of completely random measures—0
Differentially Private Regression for Discrete-Time Survival Analysis—0
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss—0
Clinical Characteristics and Laboratory Biomarkers in ICU-admitted Septic Patients with and without Bacteremia—0
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