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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 76–100 of 472 papers

TitleStatusHype
Stop Chasing the C-index: This Is How We Should Evaluate Our Survival Models—0
A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis—0
CardioCoT: Hierarchical Reasoning for Multimodal Survival Analysis—0
Will Large Language Models Transform Clinical Prediction?—0
Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare—0
SurvUnc: A Meta-Model Based Uncertainty Quantification Framework for Survival AnalysisCode0
Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data—0
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification—0
Too Sick for Working, or Sick of Working? Impact of Acute Health Shocks on Early Labour Market Exits—0
STG: Spatiotemporal Graph Neural Network with Fusion and Spatiotemporal Decoupling Learning for Prognostic Prediction of Colorectal Cancer Liver Metastasis—0
Learning Survival Distributions with the Asymmetric Laplace Distribution—0
Prediction of Delirium Risk in Mild Cognitive Impairment Using Time-Series data, Machine Learning and Comorbidity Patterns -- A Retrospective Study—0
TV-SurvCaus: Dynamic Representation Balancing for Causal Survival Analysis—0
Predictive Multiplicity in Survival Models: A Method for Quantifying Model Uncertainty in Predictive Maintenance Applications—0
Offline Dynamic Inventory and Pricing Strategy: Addressing Censored and Dependent DemandCode0
Forecasting from Clinical Textual Time Series: Adaptations of the Encoder and Decoder Language Model Families—0
Predicting the Lifespan of Industrial Printheads with Survival Analysis—0
ICBM community cancer registry analysis: a focus on Non-Hodgkin Lymphoma cases in missileers—0
Interpretable Non-linear Survival Analysis with Evolutionary Symbolic RegressionCode0
Vision Transformers with Autoencoders and Explainable AI for Cancer Patient Risk Stratification Using Whole Slide Imaging—0
Extending Cox Proportional Hazards Model with Symbolic Non-Linear Log-Risk Functions for Survival AnalysisCode0
Improving Diseases Predictions Utilizing External Bio-Banks—0
AdaMHF: Adaptive Multimodal Hierarchical Fusion for Survival PredictionCode0
Interpretable Deep Regression Models with Interval-Censored Failure Time Data—0
VGAT: A Cancer Survival Analysis Framework Transitioning from Generative Visual Question Answering to Genomic ReconstructionCode0
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