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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 121–130 of 472 papers

TitleStatusHype
Actionable Recourse via GANs for Mobile Health—0
A unified construction for series representations and finite approximations of completely random measures—0
Dynamic prediction of time to event with survival curves—0
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss—0
Deep Learning Approach for Predicting 30 Day Readmissions after Coronary Artery Bypass Graft Surgery—0
Deep Learning for Cancer Prognosis Prediction Using Portrait Photos by StyleGAN Embedding—0
Clinical Characteristics and Laboratory Biomarkers in ICU-admitted Septic Patients with and without Bacteremia—0
An Approach for Clustering Subjects According to Similarities in Cell Distributions within Biopsies—0
A Cost-Aware Approach to Adversarial Robustness in Neural Networks—0
Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles—0
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