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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 1–25 of 472 papers

TitleStatusHype
Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studies—0
Soft decision trees for survival analysis—0
Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs—0
Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence—0
Survival Analysis as Imprecise Classification with Trainable KernelsCode0
Analyzing Breast Cancer Survival Disparities by Race and Demographic Location: A Survival Analysis Approach—0
Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planning—0
A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis—0
Distributionally Robust Learning in Survival Analysis—0
Stop Chasing the C-index: This Is How We Should Evaluate Our Survival Models—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
Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality RebalanceCode1
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
Learning Survival Distributions with the Asymmetric Laplace Distribution—0
STG: Spatiotemporal Graph Neural Network with Fusion and Spatiotemporal Decoupling Learning for Prognostic Prediction of Colorectal Cancer Liver Metastasis—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
Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual RepresentationCode1
Beyond Cox Models: Assessing the Performance of Machine-Learning Methods in Non-Proportional Hazards and Non-Linear Survival AnalysisCode1
Predictive Multiplicity in Survival Models: A Method for Quantifying Model Uncertainty in Predictive Maintenance Applications—0
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