Survival Analysis
133 papers with code • 0 benchmarks • 4 datasets
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.
Benchmarks
These leaderboards are used to track progress in Survival Analysis
Libraries
Use these libraries to find Survival Analysis models and implementationsDatasets
GBSG2
Most implemented papers
Learning Genomic Representations to Predict Clinical Outcomes in Cancer
Genomics are rapidly transforming medical practice and basic biomedical research, providing insights into disease mechanisms and improving therapeutic strategies, particularly in cancer.
Contextual Explanation Networks
Our results on image and text classification and survival analysis tasks demonstrate that CENs are not only competitive with the state-of-the-art methods but also offer additional insights behind each prediction, that can be valuable for decision support.
GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settings
This article presents GuideR, a user-guided rule induction algorithm, which overcomes the largest limitation of the existing methods-the lack of the possibility to introduce user's preferences or domain knowledge to the rule learning process.
Learning to rank for censored survival data
Museau/Survival_Pytorch_EMD
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Survival analysis is a type of semi-supervised ranking task where the target output (the survival time) is often right-censored.
A Recurrent Neural Network Survival Model: Predicting Web User Return Time
We develop a novel RNN survival model that removes the limitations of the state of the art methods.
Binacox: automatic cut-point detection in high-dimensional Cox model with applications in genetics
We introduce the binacox, a prognostic method to deal with the problem of detecting multiple cut-points per features in a multivariate setting where a large number of continuous features are available.
Deep Recurrent Survival Analysis
rk2900/drsa
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By capturing the time dependency through modeling the conditional probability of the event for each sample, our method predicts the likelihood of the true event occurrence and estimates the survival rate over time, i. e., the probability of the non-occurrence of the event, for the censored data.
Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks
zhangquan-ut/Lomax-delegate-racing-for-survival-analysis-with-competing-risks
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NeurIPS 2018
We propose Lomax delegate racing (LDR) to explicitly model the mechanism of survival under competing risks and to interpret how the covariates accelerate or decelerate the time to event.
Feature Selection for Survival Analysis with Competing Risks using Deep Learning
Deep learning models for survival analysis have gained significant attention in the literature, but they suffer from severe performance deficits when the dataset contains many irrelevant features.
Multivariate Arrival Times with Recurrent Neural Networks for Personalized Demand Forecasting
rubikloud/matrnn
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However, buyer purchase patterns are extremely diverse and sparse on a per-product level due to population heterogeneity as well as dependence in purchase patterns across product categories.
