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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

Showing 271280 of 472 papers

TitleStatusHype
Actionable Recourse via GANs for Mobile Health0
Multimodal Learning for Non-small Cell Lung Cancer Prognosis0
A Latent Space Model for HLA Compatibility Networks in Kidney Transplantation0
Reverse Survival Model (RSM): A Pipeline for Explaining Predictions of Deep Survival Models0
Predicting Survival Outcomes in the Presence of Unlabeled Data0
Dynamic Survival Transformers for Causal Inference with Electronic Health Records0
Deep conditional transformation models for survival analysis0
Integrative Pan-Cancer Analysis of RNMT: a Potential Prognostic and Immunological Biomarker0
EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological ImagesCode0
Factor-Augmented Regularized Model for Hazard Regression0
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