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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 3140 of 472 papers

TitleStatusHype
Predicting Survivability of Cancer Patients with Metastatic Patterns Using Explainable AICode1
Vision Transformers with Autoencoders and Explainable AI for Cancer Patient Risk Stratification Using Whole Slide Imaging0
Extending Cox Proportional Hazards Model with Symbolic Non-Linear Log-Risk Functions for Survival AnalysisCode0
Improving Diseases Predictions Utilizing External Bio-Banks0
AdaMHF: Adaptive Multimodal Hierarchical Fusion for Survival PredictionCode0
VGAT: A Cancer Survival Analysis Framework Transitioning from Generative Visual Question Answering to Genomic ReconstructionCode0
Interpretable Deep Regression Models with Interval-Censored Failure Time Data0
A novel gradient-based method for decision trees optimizing arbitrary differential loss functionsCode0
Ensemble Survival Analysis for Preclinical Cognitive Decline Prediction in Alzheimer's Disease Using Longitudinal Biomarkers0
Survival Analysis with Machine Learning for Predicting Li-ion Battery Remaining Useful LifeCode1
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