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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 251–275 of 472 papers

TitleStatusHype
Optimization of Velocity Ramps with Survival Analysis for Intersection Merge-Ins—0
Intersection Warning System for Occlusion Risks using Relational Local Dynamic Maps—0
Probabilistic Uncertainty-Aware Risk Spot Detector for Naturalistic Driving—0
Penalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patients—0
A Statistical Learning Take on the Concordance Index for Survival Analysis—0
Federated Survival ForestsCode0
Towards inferring network properties from epidemic data—0
The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligence—0
Heterogeneous Datasets for Federated Survival Analysis SimulationCode0
Deep Clustering Survival Machines with Interpretable Expert DistributionsCode0
Maximum Mean Discrepancy Kernels for Predictive and Prognostic Modeling of Whole Slide ImagesCode0
Case-Base Neural Networks: survival analysis with time-varying, higher-order interactionsCode0
Novelty Detection in Network Traffic: Using Survival Analysis for Feature Identification—0
Hospital transfer risk prediction for COVID-19 patients from a medicalized hotel based on Diffusion GraphSAGE—0
Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithm—0
Examining marginal properness in the external validation of survival models with squared and logarithmic lossesCode0
Multi-Source Survival Domain AdaptationCode0
Distributionally Robust Survival Analysis: A Novel Fairness Loss Without DemographicsCode0
Variable selection for nonlinear Cox regression model via deep learning—0
Extending the Neural Additive Model for Survival Analysis with EHR Data—0
Actionable Recourse via GANs for Mobile Health—0
Multimodal Learning for Non-small Cell Lung Cancer Prognosis—0
A Latent Space Model for HLA Compatibility Networks in Kidney Transplantation—0
Reverse Survival Model (RSM): A Pipeline for Explaining Predictions of Deep Survival Models—0
Predicting Survival Outcomes in the Presence of Unlabeled Data—0
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