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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 311320 of 472 papers

TitleStatusHype
Towards simple time-to-event modeling: optimizing neural networks via rank regression0
Simpler Calibration for Survival Analysis0
Assumption-Free Survival Analysis Under Local Smoothness Prior0
Early ICU Mortality Prediction and Survival Analysis for Respiratory Failure0
AMMASurv: Asymmetrical Multi-Modal Attention for Accurate Survival Analysis with Whole Slide Images and Gene Expression Data0
Deep survival analysis with longitudinal X-rays for COVID-190
ALBRT: Cellular Composition Prediction in Routine Histology ImagesCode0
Individual Survival Curves with Conditional Normalizing Flows0
DeepMMSA: A Novel Multimodal Deep Learning Method for Non-small Cell Lung Cancer Survival Analysis0
Locally Sparse Neural Networks for Tabular Biomedical DataCode1
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