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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 1–50 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
Forecasting from Clinical Textual Time Series: Adaptations of the Encoder and Decoder Language Model Families—0
Offline Dynamic Inventory and Pricing Strategy: Addressing Censored and Dependent DemandCode0
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
Predicting Survivability of Cancer Patients with Metastatic Patterns Using Explainable AICode1
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
VGAT: A Cancer Survival Analysis Framework Transitioning from Generative Visual Question Answering to Genomic ReconstructionCode0
Interpretable Deep Regression Models with Interval-Censored Failure Time Data—0
A novel gradient-based method for decision trees optimizing arbitrary differential loss functionsCode0
Ensemble Survival Analysis for Preclinical Cognitive Decline Prediction in Alzheimer's Disease Using Longitudinal Biomarkers—0
Survival Analysis with Machine Learning for Predicting Li-ion Battery Remaining Useful LifeCode1
Generalized Bayesian Ensemble Survival Tree (GBEST) model—0
Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event DataCode0
Attention-Based Synthetic Data Generation for Calibration-Enhanced Survival Analysis: A Case Study for Chronic Kidney Disease Using Electronic Health Records—0
Adaptive Prototype Learning for Multimodal Cancer Survival AnalysisCode0
MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and RetentionCode1
Enhancing Collaborative Filtering-Based Course Recommendations by Exploiting Time-to-Event Information with Survival Analysis—0
Overcoming Dependent Censoring in the Evaluation of Survival ModelsCode0
Enhancing External Validity of Experiments with Ongoing Sampling—0
Censor Dependent Variational InferenceCode0
4D VQ-GAN: Synthesising Medical Scans at Any Time Point for Personalised Disease Progression Modelling of Idiopathic Pulmonary Fibrosis—0
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