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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 71–80 of 472 papers

TitleStatusHype
Enhanced Lung Cancer Survival Prediction using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets—0
RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency—0
Graph Domain Adaptation with Dual-branch Encoder and Two-level Alignment for Whole Slide Image-based Survival Prediction—0
Integrated Machine Learning and Survival Analysis Modeling for Enhanced Chronic Kidney Disease Risk StratificationCode0
Evidential time-to-event prediction with calibrated uncertainty quantification—0
DNAMite: Interpretable Calibrated Survival Analysis with Discretized Additive ModelsCode0
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models—0
Masked Clinical Modelling: A Framework for Synthetic and Augmented Survival Data Generation—0
Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks—0
HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing RisksCode0
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