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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 301–350 of 472 papers

TitleStatusHype
Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets—0
Federated Survival Analysis with Discrete-Time Cox Models—0
FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysis—0
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications—0
Forecasting Automotive Supply Chain Shortfalls with Heterogeneous Time Series—0
Forecasting from Clinical Textual Time Series: Adaptations of the Encoder and Decoder Language Model Families—0
FPBoost: Fully Parametric Gradient Boosting for Survival Analysis—0
From Non-Paying to Premium: Predicting User Conversion in Video Games with Ensemble Learning—0
From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba—0
Gaussian Processes for Survival Analysis—0
Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy—0
Generalized Bayesian Ensemble Survival Tree (GBEST) model—0
Global Censored Quantile Random Forest—0
Graph Domain Adaptation with Dual-branch Encoder and Two-level Alignment for Whole Slide Image-based Survival Prediction—0
GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization—0
Hazard function models to estimate mortality rates affecting fish populations with application to the sea mullet (Mugil cephalus) fishery on the Queensland coast (Australia)—0
Hazard Gradient Penalty for 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
Hospital transfer risk prediction for COVID-19 patients from a medicalized hotel based on Diffusion GraphSAGE—0
Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification—0
ICBM community cancer registry analysis: a focus on Non-Hodgkin Lymphoma cases in missileers—0
Identification of Cancer Patient Subgroups via Smoothed Shortest Path Graph Kernel—0
Image-based Survival Analysis for Lung Cancer Patients using CNNs—0
Improved joint modelling of breast cancer radiomics features and hazard by image registration aided longitudinal CT data—0
Improving Diseases Predictions Utilizing External Bio-Banks—0
Improving Event Time Prediction by Learning to Partition the Event Time Space—0
Individual Survival Curves with Conditional Normalizing Flows—0
Integrative Pan-Cancer Analysis of RNMT: a Potential Prognostic and Immunological Biomarker—0
Interpretable Deep Regression Models with Interval-Censored Failure Time Data—0
Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions—0
Interpretable Prediction and Feature Selection for Survival Analysis—0
Interpretable Survival Analysis for Heart Failure Risk Prediction—0
Intersection Warning System for Occlusion Risks using Relational Local Dynamic Maps—0
iSurvive: An Interpretable, Event-time Prediction Model for mHealth—0
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data—0
KL-divergence Based Deep Learning for Discrete Time Model—0
Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors—0
Learning Survival Distributions with the Asymmetric Laplace Distribution—0
Likelihood-Free Dynamical Survival Analysis Applied to the COVID-19 Epidemic in Ohio—0
Likelihood Ratio Confidence Sets for Sequential Decision Making—0
Deep Recurrent Survival AnalysisCode0
Energy-based survival modelling using harmoniumsCode0
Survival Analysis as Imprecise Classification with Trainable KernelsCode0
Deep Neural Networks for Survival Analysis Based on a Multi-Task FrameworkCode0
Deep Learning for Patient-Specific Kidney Graft Survival AnalysisCode0
An Efficient Training Algorithm for Kernel Survival Support Vector MachinesCode0
Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival DataCode0
Maximum Mean Discrepancy Kernels for Predictive and Prognostic Modeling of Whole Slide ImagesCode0
A Multi-Modal Deep Learning Framework for Pan-Cancer PrognosisCode0
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