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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

Showing 101110 of 472 papers

TitleStatusHype
A novel gradient-based method for decision trees optimizing arbitrary differential loss functionsCode0
Ensemble Survival Analysis for Preclinical Cognitive Decline Prediction in Alzheimer's Disease Using Longitudinal Biomarkers0
Generalized Bayesian Ensemble Survival Tree (GBEST) model0
Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event DataCode0
Attention-Based Synthetic Data Generation for Calibration-Enhanced Survival Analysis: A Case Study for Chronic Kidney Disease Using Electronic Health Records0
Adaptive Prototype Learning for Multimodal Cancer Survival AnalysisCode0
Enhancing Collaborative Filtering-Based Course Recommendations by Exploiting Time-to-Event Information with Survival Analysis0
Overcoming Dependent Censoring in the Evaluation of Survival ModelsCode0
Enhancing External Validity of Experiments with Ongoing Sampling0
Censor Dependent Variational InferenceCode0
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