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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 76100 of 472 papers

TitleStatusHype
A Study on Survival Analysis Methods Using Neural Network to Prevent CancersCode0
A General Machine Learning Framework for Survival AnalysisCode0
A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular InformationCode0
Forecasting Disease Progression with Parallel Hyperplanes in Longitudinal Retinal OCTCode0
GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settingsCode0
A Scalable Discrete-Time Survival Model for Neural NetworksCode0
Adaptive Prototype Learning for Multimodal Cancer Survival AnalysisCode0
A Recurrent Neural Network Survival Model: Predicting Web User Return TimeCode0
Federated Survival ForestsCode0
AdaMHF: Adaptive Multimodal Hierarchical Fusion for Survival PredictionCode0
A novel gradient-based method for decision trees optimizing arbitrary differential loss functionsCode0
Conformalized Survival AnalysisCode0
Computing the Hazard Ratios Associated with Explanatory Variables Using Machine Learning Models of Survival DataCode0
Fairness in Survival Analysis with Distributionally Robust OptimizationCode0
Feature Selection for Survival Analysis with Competing Risks using Deep LearningCode0
Flexible Group Fairness Metrics for Survival AnalysisCode0
HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing RisksCode0
Dynamic Survival Analysis for non-Markovian Epidemic ModelsCode0
Energy-based survival modelling using harmoniumsCode0
Dynamical Survival Analysis with Controlled Latent StatesCode0
Conditioning on Time is All You Need for Synthetic Survival Data GenerationCode0
Dynamic Entity-Masked Graph Diffusion Model for histopathological image Representation LearningCode0
Conformalized Survival Distributions: A Generic Post-Process to Increase CalibrationCode0
EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological ImagesCode0
An Efficient Training Algorithm for Kernel Survival Support Vector MachinesCode0
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