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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 126–150 of 472 papers

TitleStatusHype
A Differentially Private Kaplan-Meier Estimator for Privacy-Preserving Survival Analysis—0
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss—0
EsurvFusion: An evidential multimodal survival fusion model based on Gaussian random fuzzy numbers—0
Enhanced Lung Cancer Survival Prediction using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets—0
RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency—0
Graph Domain Adaptation with Dual-branch Encoder and Two-level Alignment for Whole Slide Image-based Survival Prediction—0
Integrated Machine Learning and Survival Analysis Modeling for Enhanced Chronic Kidney Disease Risk StratificationCode0
Evidential time-to-event prediction with calibrated uncertainty quantification—0
DNAMite: Interpretable Calibrated Survival Analysis with Discretized Additive ModelsCode0
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models—0
Masked Clinical Modelling: A Framework for Synthetic and Augmented Survival Data Generation—0
Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks—0
HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing RisksCode0
Predicting Breast Cancer Survival: A Survival Analysis Approach Using Log Odds and Clinical Variables—0
Global Censored Quantile Random Forest—0
End-Stage Liver Disease Comorbidities in Patients Awaiting Transplantation: Identification and Impact on Liver Transplant Survival—0
Toward a Well-Calibrated Discrimination via Survival Outcome-Aware Contrastive Learning—0
Deep End-to-End Survival Analysis with Temporal Consistency—0
SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural Network—0
Forecasting Disease Progression with Parallel Hyperplanes in Longitudinal Retinal OCTCode0
Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling—0
FPBoost: Fully Parametric Gradient Boosting for Survival Analysis—0
SeqRisk: Transformer-augmented latent variable model for improved survival prediction with longitudinal data—0
A Cost-Aware Approach to Adversarial Robustness in Neural Networks—0
Adaptive Transformer Modelling of Density Function for Nonparametric Survival AnalysisCode0
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