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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 271–280 of 472 papers

TitleStatusHype
Weighted Concordance Index Loss-based Multimodal Survival Modeling for Radiation Encephalopathy Assessment in Nasopharyngeal Carcinoma Radiotherapy—0
Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy GuaranteeCode0
Quantitative CT texture-based method to predict diagnosis and prognosis of fibrosing interstitial lung disease patterns—0
User Engagement in Mobile Health Applications—0
A Multiple kernel testing procedure for non-proportional hazards in factorial designs—0
Neural interval-censored survival regression with feature selectionCode2
No-regret Learning in Repeated First-Price Auctions with Budget Constraints—0
Survival Analysis on Structured Data using Deep Reinforcement Learning—0
Hazard Gradient Penalty for Survival Analysis—0
Flexible Group Fairness Metrics for Survival AnalysisCode0
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