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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 151175 of 472 papers

TitleStatusHype
Differentially Private Distributed InferenceCode0
Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival DataCode0
Binacox: automatic cut-point detection in high-dimensional Cox model with applications in geneticsCode0
ICTSurF: Implicit Continuous-Time Survival Functions with Neural NetworksCode0
Case-Base Neural Networks: survival analysis with time-varying, higher-order interactionsCode0
Dynamic Survival Analysis for non-Markovian Epidemic ModelsCode0
Interpretable Non-linear Survival Analysis with Evolutionary Symbolic RegressionCode0
Gradient Boosting Survival Tree with Applications in Credit ScoringCode0
GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settingsCode0
Forecasting Disease Progression with Parallel Hyperplanes in Longitudinal Retinal OCTCode0
Censor Dependent Variational InferenceCode0
ALBRT: Cellular Composition Prediction in Routine Histology ImagesCode0
Gene-MOE: A sparsely gated prognosis and classification framework exploiting pan-cancer genomic informationCode0
HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing RisksCode0
Deep Recurrent Survival AnalysisCode0
Feature Selection for Survival Analysis with Competing Risks using Deep LearningCode0
Deep Neural Networks for Survival Analysis Based on a Multi-Task FrameworkCode0
Fairness in Survival Analysis with Distributionally Robust OptimizationCode0
EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological ImagesCode0
Federated Survival ForestsCode0
CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learningCode0
Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency RatesCode0
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measuresCode0
A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional DataCode0
Exploring the Wasserstein metric for survival analysisCode0
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