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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 6170 of 472 papers

TitleStatusHype
Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal CancerCode1
Deep Learning for Survival Analysis: A ReviewCode1
iMD4GC: Incomplete Multimodal Data Integration to Advance Precise Treatment Response Prediction and Survival Analysis for Gastric CancerCode1
MoME: Mixture of Multimodal Experts for Cancer Survival PredictionCode1
Sensitivity of Survival Analysis MetricsCode1
Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression LabelsCode1
Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modelingCode1
A kernel log-rank test of independence for right-censored dataCode0
Deep Neural Networks for Survival Analysis Based on a Multi-Task FrameworkCode0
Learning Survival Distribution with Implicit Survival FunctionCode0
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