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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 276–300 of 472 papers

TitleStatusHype
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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