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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 181–190 of 472 papers

TitleStatusHype
Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis—0
Efficient Training of Probabilistic Neural Networks for Survival AnalysisCode0
Analyzing Economic Convergence Across the Americas: A Survival Analysis Approach to GDP per Capita Trajectories—0
Shared Hardships Strengthen Bonds: Negative Shocks, Embeddedness and Employee Retention—0
On Training Survival Models with Scoring Rules—0
Dynamic Survival Analysis for Early Event Prediction—0
Interpretable Machine Learning for Survival AnalysisCode0
Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival DataCode0
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission—0
A network-constrain Weibull AFT model for biomarkers discovery—0
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