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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 361–370 of 472 papers

TitleStatusHype
Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features RelateCode0
Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal CancerCode1
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival AnalysisCode0
Predictive Analytics for Water Asset Management: Machine Learning and Survival Analysis—0
An Approach for Clustering Subjects According to Similarities in Cell Distributions within Biopsies—0
Predicting Customer Churn in World of Warcraft—0
A General Machine Learning Framework for Survival AnalysisCode0
BoXHED: Boosted eXact Hazard Estimator with Dynamic covariatesCode0
A Causally Formulated Hazard Ratio Estimation through Backdoor Adjustment on Structural Causal Model—0
Federated Survival Analysis with Discrete-Time Cox Models—0
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