SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–300 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
Dynamic Survival Transformers for Causal Inference with Electronic Health Records—0
Deep conditional transformation models for survival analysis—0
Integrative Pan-Cancer Analysis of RNMT: a Potential Prognostic and Immunological Biomarker—0
EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological ImagesCode0
Factor-Augmented Regularized Model for Hazard Regression—0
Temporal Pattern Mining for Analysis of Longitudinal Clinical Data: Identifying Risk Factors for Alzheimer's Disease—0
Copula Entropy based Variable Selection for Survival AnalysisCode0
Temporal Label Smoothing for Early Event PredictionCode0
Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review—0
Necessary and sufficient conditions for exact closures of epidemic equations on configuration model networks—0
KL-divergence Based Deep Learning for Discrete Time Model—0
Multi-Scale User Behavior Network for Entire Space Multi-Task Learning—0
Child Care Provider Survival Analysis—0
Likelihood-Free Dynamical Survival Analysis Applied to the COVID-19 Epidemic in Ohio—0
Open-radiomics: A Collection of Standardized Datasets and a Technical Protocol for Reproducible Radiomics Machine Learning Pipelines—0
Subtype-Former: a deep learning approach for cancer subtype discovery with multi-omics data—0
Pseudo value-based Deep Neural Networks for Multi-state Survival Analysis—0
FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysis—0
A State Transition Model for Mobile Notifications via Survival Analysis—0
Weighted Concordance Index Loss-based Multimodal Survival Modeling for Radiation Encephalopathy Assessment in Nasopharyngeal Carcinoma Radiotherapy—0
Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy GuaranteeCode0
Quantitative CT texture-based method to predict diagnosis and prognosis of fibrosing interstitial lung disease patterns—0
User Engagement in Mobile Health Applications—0
A Multiple kernel testing procedure for non-proportional hazards in factorial designs—0
No-regret Learning in Repeated First-Price Auctions with Budget Constraints—0
Show:102550
← PrevPage 6 of 10Next →

No leaderboard results yet.