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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 231240 of 472 papers

TitleStatusHype
Deep Learning for Cancer Prognosis Prediction Using Portrait Photos by StyleGAN Embedding0
Cross-Validation Is All You Need: A Statistical Approach To Label Noise EstimationCode0
Structured Learning in Time-dependent Cox ModelsCode0
Copula-Based Deep Survival Models for Dependent Censoring0
Predicting Risk of Dementia with Survival Machine Learning and Statistical Methods: Results on the English Longitudinal Study of Ageing Cohort0
Transformer-based Time-to-Event Prediction for Chronic Kidney Disease DeteriorationCode0
Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers0
Learning Survival Distribution with Implicit Survival FunctionCode0
survAIval: Survival Analysis with the Eyes of AI0
Neural Fine-Gray: Monotonic neural networks for competing risksCode0
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