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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 1120 of 472 papers

TitleStatusHype
Predicting Survivability of Cancer Patients with Metastatic Patterns Using Explainable AICode1
Survival Analysis with Machine Learning for Predicting Li-ion Battery Remaining Useful LifeCode1
MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and RetentionCode1
SurvHive: a package to consistently access multiple survival-analysis packagesCode1
Tackling Small Sample Survival Analysis via Transfer Learning: A Study of Colorectal Cancer PrognosisCode1
An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event OutcomesCode1
CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival AnalysisCode1
Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression LabelsCode1
Multimodal Cross-Task Interaction for Survival Analysis in Whole Slide Pathological ImagesCode1
A Closer Look at Mortality Risk Prediction from ElectrocardiogramsCode1
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