Survival analysis in Python
-
Updated
Oct 29, 2024 - Python
Survival analysis in Python
Fast Best-Subset Selection Library
Explainable Machine Learning in Survival Analysis
COX Proportional risk model and survival analysis implemented by tensorflow.
Resources for Survival Analysis
SurvSHAP(t): Time-dependent explanations of machine learning survival models
Survival analysis in Julia
Perform a survival analysis based on the time-to-event (death event) for the subjects. Compare machine learning models to assess the likelihood of a death by heart failure condition. This can be used to help hospitals in assessing the severity of patients with cardiovascular diseases and heart failure condition.
Code for the paper "Deep Cox Mixtures for Survival Regression", Machine Learning for Healthcare Conference 2021
Code repository for the manuscript: 'Assessing performance in prediction models with survival outcomes: practical guidance for Cox proportional hazards models' (published in Annals of Internal Medicine)
geneSurv: an interactive web-based tool for survival analysis in genomics research
snpnet - Efficient Lasso Solver for Large-scale genetic variant data
ISMB 2020: Improved survival analysis by learning shared genomic information from pan-cancer data
R material for LSHTM's Advanced Statistical Methods in Epidemiology (ASME) practical sessions
Survival analysis utility functions using functional programming principals
A 30+ node flowchart for selecting the right statistical test for evaluating experimental data.
psfmi: Predictor Selection Functions for Logistic and Cox regression models in multiply imputed datasets
Deep learning prediction on BRAF genetic mutation for patients with pediatric low grade glioma (pLGG).
Health app, that predicts diabetic retinopathy
Add a description, image, and links to the cox-regression topic page so that developers can more easily learn about it.
To associate your repository with the cox-regression topic, visit your repo's landing page and select "manage topics."