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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 281–290 of 472 papers

TitleStatusHype
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
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