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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 276–300 of 472 papers

TitleStatusHype
Early ICU Mortality Prediction and Survival Analysis for Respiratory Failure—0
Embedding-based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomes—0
End-Stage Liver Disease Comorbidities in Patients Awaiting Transplantation: Identification and Impact on Liver Transplant Survival—0
End-to-end Multi-source Visual Prompt Tuning for Survival Analysis in Whole Slide Images—0
Enhanced Lung Cancer Survival Prediction using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets—0
Enhancing Collaborative Filtering-Based Course Recommendations by Exploiting Time-to-Event Information with Survival Analysis—0
Enhancing External Validity of Experiments with Ongoing Sampling—0
Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare—0
Ensemble Survival Analysis for Preclinical Cognitive Decline Prediction in Alzheimer's Disease Using Longitudinal Biomarkers—0
EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, Featuring Prognostic Stratification Boosting—0
Estimating and interpreting secondary attack risk: Binomial considered harmful—0
Estimating Heterogenous Treatment Effects for Survival Data with Doubly Doubly Robust Estimator—0
Estimation of conditional mixture Weibull distribution with right-censored data using neural network for time-to-event analysis—0
Estimation of Time-to-Total Knee Replacement Surgery—0
EsurvFusion: An evidential multimodal survival fusion model based on Gaussian random fuzzy numbers—0
Evidential time-to-event prediction with calibrated uncertainty quantification—0
Explainable AI for survival analysis: a median-SHAP approach—0
Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review—0
Explainable Survival Analysis with Uncertainty using Convolution-Involved Vision Transformer—0
Exploring novel prognostic biomarkers and biologic processes involved in NASH, cirrhosis and HCC based on survival analysis using systems biology approach—0
Extending the Neural Additive Model for Survival Analysis with EHR Data—0
Factor-Augmented Regularized Model for Hazard Regression—0
Fairness in Survival Analysis: A Novel Conditional Mutual Information Augmentation Approach—0
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models—0
Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets—0
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