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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
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
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