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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 201–225 of 472 papers

TitleStatusHype
FPBoost: Fully Parametric Gradient Boosting for Survival Analysis—0
From Non-Paying to Premium: Predicting User Conversion in Video Games with Ensemble Learning—0
Discrete Stochastic Models in Continuous Time for Ecology—0
Gaussian Processes for Survival Analysis—0
Continuous and Discrete-Time Survival Prediction with Neural Networks—0
Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy—0
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer—0
Global Censored Quantile Random Forest—0
Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records—0
Graph Domain Adaptation with Dual-branch Encoder and Two-level Alignment for Whole Slide Image-based Survival Prediction—0
Improving Diseases Predictions Utilizing External Bio-Banks—0
Interpretable Prediction and Feature Selection for Survival Analysis—0
GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization—0
Copula-Based Deep Survival Models for Dependent Censoring—0
KL-divergence Based Deep Learning for Discrete Time Model—0
Differentially Private Regression for Discrete-Time Survival Analysis—0
Development of digitally obtainable 10-year risk scores for depression and anxiety in the general population—0
Hazard Gradient Penalty for Survival Analysis—0
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification—0
A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis—0
High-Dimensional False Discovery Rate Control for Dependent Variables—0
Higher Mediterranean diet score is associated with longer time between relapses in Australian females with multiple sclerosis—0
Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction—0
Identification of Cancer Patient Subgroups via Smoothed Shortest Path Graph Kernel—0
Image-based Survival Analysis for Lung Cancer Patients using CNNs—0
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