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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 176–200 of 472 papers

TitleStatusHype
EsurvFusion: An evidential multimodal survival fusion model based on Gaussian random fuzzy numbers—0
Evidential time-to-event prediction with calibrated uncertainty quantification—0
Explainable AI for survival analysis: a median-SHAP approach—0
Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review—0
Explainable Survival Analysis with Uncertainty using Convolution-Involved Vision Transformer—0
Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs—0
Exploring novel prognostic biomarkers and biologic processes involved in NASH, cirrhosis and HCC based on survival analysis using systems biology approach—0
Discrete Stochastic Models in Continuous Time for Ecology—0
Extending the Neural Additive Model for Survival Analysis with EHR Data—0
Factor-Augmented Regularized Model for Hazard Regression—0
Fairness in Survival Analysis: A Novel Conditional Mutual Information Augmentation Approach—0
Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework—0
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer—0
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models—0
Composite Survival Analysis: Learning with Auxiliary Aggregated Baselines and Survival Scores—0
Graph Domain Adaptation with Dual-branch Encoder and Two-level Alignment for Whole Slide Image-based Survival Prediction—0
High-Dimensional False Discovery Rate Control for Dependent Variables—0
An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes—0
FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysis—0
ICBM community cancer registry analysis: a focus on Non-Hodgkin Lymphoma cases in missileers—0
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications—0
Differentially Private Regression for Discrete-Time Survival Analysis—0
KL-divergence Based Deep Learning for Discrete Time Model—0
Development of digitally obtainable 10-year risk scores for depression and anxiety in the general population—0
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification—0
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