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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
Dynamic Survival Transformers for Causal Inference with Electronic Health Records—0
Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework—0
Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records—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
Copula-Based Deep Survival Models for Dependent Censoring—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
Assumption-Free Survival Analysis Under Local Smoothness Prior—0
CNN-based Survival Model for Pancreatic Ductal Adenocarcinoma in Medical Imaging—0
A network-constrain Weibull AFT model for biomarkers discovery—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
Deep conditional transformation models for survival analysis—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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