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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 101125 of 472 papers

TitleStatusHype
Contextual Explanation NetworksCode0
Continuous and Discrete-Time Survival Prediction with Neural NetworksCode0
Gradient Boosting Survival Tree with Applications in Credit ScoringCode0
A Recurrent Neural Network Survival Model: Predicting Web User Return TimeCode0
SimHawNet: A Modified Hawkes Process for Temporal Network SimulationCode0
HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing RisksCode0
An Efficient Training Algorithm for Kernel Survival Support Vector MachinesCode0
Clustering Survival Data using a Mixture of Non-parametric ExpertsCode0
Forecasting Disease Progression with Parallel Hyperplanes in Longitudinal Retinal OCTCode0
Cross-Validation Is All You Need: A Statistical Approach To Label Noise EstimationCode0
Gene-MOE: A sparsely gated prognosis and classification framework exploiting pan-cancer genomic informationCode0
CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learningCode0
A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular InformationCode0
Deep Clustering Survival Machines with Interpretable Expert DistributionsCode0
Feature Selection for Survival Analysis with Competing Risks using Deep LearningCode0
A General Machine Learning Framework for Survival AnalysisCode0
Extending Cox Proportional Hazards Model with Symbolic Non-Linear Log-Risk Functions for Survival AnalysisCode0
A Deep Learning Approach for Overall Survival Prediction in Lung Cancer with Missing ValuesCode0
Fairness in Survival Analysis with Distributionally Robust OptimizationCode0
Federated Survival ForestsCode0
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measuresCode0
DeepHit: A Deep Learning Approach to Survival Analysis with Competing RisksCode0
Censor Dependent Variational InferenceCode0
Deep Landscape Forecasting for Real-time Bidding AdvertisingCode0
EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological ImagesCode0
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