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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
Prediction of Delirium Risk in Mild Cognitive Impairment Using Time-Series data, Machine Learning and Comorbidity Patterns -- A Retrospective Study0
TV-SurvCaus: Dynamic Representation Balancing for Causal Survival Analysis0
Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual RepresentationCode1
Beyond Cox Models: Assessing the Performance of Machine-Learning Methods in Non-Proportional Hazards and Non-Linear Survival AnalysisCode1
Predictive Multiplicity in Survival Models: A Method for Quantifying Model Uncertainty in Predictive Maintenance Applications0
Forecasting from Clinical Textual Time Series: Adaptations of the Encoder and Decoder Language Model Families0
Offline Dynamic Inventory and Pricing Strategy: Addressing Censored and Dependent DemandCode0
Predicting the Lifespan of Industrial Printheads with Survival Analysis0
ICBM community cancer registry analysis: a focus on Non-Hodgkin Lymphoma cases in missileers0
Interpretable Non-linear Survival Analysis with Evolutionary Symbolic RegressionCode0
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