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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 2130 of 472 papers

TitleStatusHype
A Deep Recurrent Survival Model for Unbiased RankingCode1
A Deep Variational Approach to Clustering Survival DataCode1
A Comparative Study for Time-to-Event Analysis and Survival Prediction for Heart Failure Condition using Machine Learning TechniquesCode1
A Closer Look at Mortality Risk Prediction from ElectrocardiogramsCode1
Adversarial Time-to-Event ModelingCode1
AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide ImagesCode1
Adaptive Sampling for Weighted Log-Rank Survival Trees BoostingCode1
Deep Learning for Survival Analysis: A ReviewCode1
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural NetworkCode1
Cohort-Individual Cooperative Learning for Multimodal Cancer Survival AnalysisCode1
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