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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 326–350 of 472 papers

TitleStatusHype
Can-SAVE: Mass Cancer Risk Prediction via Survival Analysis Variables and EHR—0
Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework—0
Composite Survival Analysis: Learning with Auxiliary Aggregated Baselines and Survival Scores—0
Continuous and Discrete-Time Survival Prediction with Neural Networks—0
Continuous Risk Measures for Driving Support—0
Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records—0
Copula-Based Deep Survival Models for Dependent Censoring—0
CoxSE: Exploring the Potential of Self-Explaining Neural Networks with Cox Proportional Hazards Model for Survival Analysis—0
Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers—0
Deep conditional transformation models for survival analysis—0
Deep Convolutional Neural Networks for Imaging Data Based Survival Analysis of Rectal Cancer—0
Deep End-to-End Survival Analysis with Temporal Consistency—0
Deep Extended Hazard Models for Survival Analysis—0
Deep Learning Approach for Predicting 30 Day Readmissions after Coronary Artery Bypass Graft Surgery—0
Deep Learning for Cancer Prognosis Prediction Using Portrait Photos by StyleGAN Embedding—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
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis—0
DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Contrast-Enhanced CT Imaging—0
Deep Survival Analysis—0
Deep survival analysis with longitudinal X-rays for COVID-19—0
Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction—0
Development of digitally obtainable 10-year risk scores for depression and anxiety in the general population—0
Differentially Private Regression for Discrete-Time Survival Analysis—0
Discrete Stochastic Models in Continuous Time for Ecology—0
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