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.

Libraries

Use these libraries to find Survival Analysis models and implementations

Latest papers with no code

Dynamic Survival Analysis for Early Event Prediction

no code yet • 19 Mar 2024

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

no code yet • 19 Mar 2024

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

no code yet • 4 Mar 2024

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

no code yet • 28 Feb 2024

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

no code yet • 7 Feb 2024

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

no code yet • 2 Feb 2024

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

no code yet • 30 Jan 2024

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

no code yet • 30 Jan 2024

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

no code yet • 28 Jan 2024

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

no code yet • 26 Jan 2024

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.