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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 251–300 of 472 papers

TitleStatusHype
The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligence—0
The TruEnd-procedure: Treating trailing zero-valued balances in credit data—0
Time-to-Event Prediction with Neural Networks and Cox Regression—0
Too Sick for Working, or Sick of Working? Impact of Acute Health Shocks on Early Labour Market Exits—0
Topic Models with Survival Supervision: Archetypal Analysis and Neural Approaches—0
Toward a Well-Calibrated Discrimination via Survival Outcome-Aware Contrastive Learning—0
Towards inferring network properties from epidemic data—0
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation—0
Towards simple time-to-event modeling: optimizing neural networks via rank regression—0
On Training Survival Models with Scoring Rules—0
TV-SurvCaus: Dynamic Representation Balancing for Causal Survival Analysis—0
Uncovering life-course patterns with causal discovery and survival analysis—0
Une comparaison des algorithmes d'apprentissage pour la survie avec données manquantes—0
Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence—0
User Engagement in Mobile Health Applications—0
Using ontology embeddings for structural inductive bias in gene expression data analysis—0
Variable selection for nonlinear Cox regression model via deep learning—0
Variable Selection with Random Survival Forest and Bayesian Additive Regression Tree for Survival Data—0
Variational Bayes survival analysis for unemployment modelling—0
Vision Transformers with Autoencoders and Explainable AI for Cancer Patient Risk Stratification Using Whole Slide Imaging—0
Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data—0
Weighted Concordance Index Loss-based Multimodal Survival Modeling for Radiation Encephalopathy Assessment in Nasopharyngeal Carcinoma Radiotherapy—0
Comparing ImageNet Pre-training with Digital Pathology Foundation Models for Whole Slide Image-Based Survival Analysis—0
Will Large Language Models Transform Clinical Prediction?—0
WSISA: Making Survival Prediction From Whole Slide Histopathological Images—0
DeepWait: Pedestrian Wait Time Estimation in Mixed Traffic Conditions Using Deep Survival Analysis—0
Your Actions or Your Associates? Predicting Certification and Dropout in MOOCs with Behavioral and Social Features—0
Unsupervised Machine Learning for the Discovery of Latent Disease Clusters and Patient Subgroups Using Electronic Health Records—0
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification—0
Stop Chasing the C-index: This Is How We Should Evaluate Our Survival Models—0
4D VQ-GAN: Synthesising Medical Scans at Any Time Point for Personalised Disease Progression Modelling of Idiopathic Pulmonary Fibrosis—0
A Causally Formulated Hazard Ratio Estimation through Backdoor Adjustment on Structural Causal Model—0
A Cost-Aware Approach to Adversarial Robustness in Neural Networks—0
Actionable Recourse via GANs for Mobile Health—0
Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks—0
Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models—0
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer—0
A Deep Latent-Variable Model Application to Select Treatment Intensity in Survival Analysis—0
A Deep Learning Approach for Dynamic Survival Analysis with Competing Risks—0
A Differentially Private Kaplan-Meier Estimator for Privacy-Preserving Survival Analysis—0
Advancing clinical trial outcomes using deep learning and predictive modelling: bridging precision medicine and patient-centered care—0
A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study—0
A Latent Space Model for HLA Compatibility Networks in Kidney Transplantation—0
A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis—0
AMMASurv: Asymmetrical Multi-Modal Attention for Accurate Survival Analysis with Whole Slide Images and Gene Expression Data—0
A Multi-modal Fusion Framework Based on Multi-task Correlation Learning for Cancer Prognosis Prediction—0
A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis—0
A Multiple kernel testing procedure for non-proportional hazards in factorial designs—0
Analyzing Breast Cancer Survival Disparities by Race and Demographic Location: A Survival Analysis Approach—0
Analyzing Economic Convergence Across the Americas: A Survival Analysis Approach to GDP per Capita Trajectories—0
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