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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 110 of 472 papers

TitleStatusHype
TorchSurv: A Lightweight Package for Deep Survival AnalysisCode2
forester: A Tree-Based AutoML Tool in RCode2
HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context InteractionCode2
Neural interval-censored survival regression with feature selectionCode2
2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image ClassificationCode2
Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational PathologyCode2
FastCPH: Efficient Survival Analysis for Neural NetworksCode2
An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event OutcomesCode1
A probabilistic estimation of remaining useful life from censored time-to-event dataCode1
A Deep Variational Approach to Clustering Survival DataCode1
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