Survival Analysis
128 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
Latest papers with no code
Dynamic Survival Analysis for Early Event Prediction
This study advances Early Event Prediction (EEP) in healthcare through Dynamic Survival Analysis (DSA), offering a novel approach by integrating risk localization into alarm policies to enhance clinical event metrics.
Training Survival Models using Scoring Rules
Survival Analysis provides critical insights for partially incomplete time-to-event data in various domains.
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission
The calibration of DeepSurv (IBS: 0. 041) performed the best, followed by RSF (IBS: 0. 042) and GBM (IBS: 0. 0421), all using the full variables.
A network-constrain Weibull AFT model for biomarkers discovery
We propose AFTNet, a novel network-constraint survival analysis method based on the Weibull accelerated failure time (AFT) model solved by a penalized likelihood approach for variable selection and estimation.
Online Learning Approach for Survival Analysis
We introduce an online mathematical framework for survival analysis, allowing real time adaptation to dynamic environments and censored data.
OPSurv: Orthogonal Polynomials Quadrature Algorithm for Survival Analysis
This paper introduces the Orthogonal Polynomials Quadrature Algorithm for Survival Analysis (OPSurv), a new method providing time-continuous functional outputs for both single and competing risks scenarios in survival analysis.
Dynamical Survival Analysis with Controlled Latent States
We consider the task of learning individual-specific intensities of counting processes from a set of static variables and irregularly sampled time series.
Explainable AI for survival analysis: a median-SHAP approach
With the adoption of machine learning into routine clinical practice comes the need for Explainable AI methods tailored to medical applications.
High-Dimensional False Discovery Rate Control for Dependent Variables
In recent years, multivariate false discovery rate (FDR) controlling methods have emerged, providing guarantees even in high-dimensional settings where the number of variables surpasses the number of samples.
SCANIA Component X Dataset: A Real-World Multivariate Time Series Dataset for Predictive Maintenance
The objective of releasing this dataset is to give a broad range of researchers the possibility of working with real-world data from an internationally well-known company and introduce a standard benchmark to the predictive maintenance field, fostering reproducible research.