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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 401–425 of 472 papers

TitleStatusHype
ICBM community cancer registry analysis: a focus on Non-Hodgkin Lymphoma cases in missileers—0
Identification of Cancer Patient Subgroups via Smoothed Shortest Path Graph Kernel—0
Image-based Survival Analysis for Lung Cancer Patients using CNNs—0
Improved joint modelling of breast cancer radiomics features and hazard by image registration aided longitudinal CT data—0
Improving Diseases Predictions Utilizing External Bio-Banks—0
Improving Event Time Prediction by Learning to Partition the Event Time Space—0
Individual Survival Curves with Conditional Normalizing Flows—0
Integrative Pan-Cancer Analysis of RNMT: a Potential Prognostic and Immunological Biomarker—0
Interpretable Deep Regression Models with Interval-Censored Failure Time Data—0
Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions—0
Interpretable Prediction and Feature Selection for Survival Analysis—0
Interpretable Survival Analysis for Heart Failure Risk Prediction—0
Intersection Warning System for Occlusion Risks using Relational Local Dynamic Maps—0
iSurvive: An Interpretable, Event-time Prediction Model for mHealth—0
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data—0
KL-divergence Based Deep Learning for Discrete Time Model—0
Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors—0
Learning Survival Distributions with the Asymmetric Laplace Distribution—0
Likelihood-Free Dynamical Survival Analysis Applied to the COVID-19 Epidemic in Ohio—0
Likelihood Ratio Confidence Sets for Sequential Decision Making—0
Long-Term Pipeline Failure Prediction Using Nonparametric Survival Analysis—0
Modeling Long Sequences in Bladder Cancer Recurrence: A Comparative Evaluation of LSTM,Transformer,and Mamba—0
Machine Learning for Survival Analysis: A Survey—0
Masked Clinical Modelling: A Framework for Synthetic and Augmented Survival Data Generation—0
MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks—0
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