Survival analysis in Python
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Updated
Mar 7, 2024 - Python
Survival analysis in Python
Fast Best-Subset Selection Library
COX Proportional risk model and survival analysis implemented by tensorflow.
Survival analysis in Julia
SurvSHAP(t): Time-dependent explanations of machine learning survival models
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 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
Explainable Machine Learning in Survival Analysis
ISMB 2020: Improved survival analysis by learning shared genomic information from pan-cancer data
Survival functions for DataSHIELD. Package for building survival models, Cox proportional hazards models and Cox regression models in DataSHIELD.
psfmi: Predictor Selection Functions for Logistic and Cox regression models in multiply imputed datasets
Survival functions (client side) for DataSHIELD. Package for building survival models, Cox proportional hazards models and Cox regression models in DataSHIELD.
Code for the paper "Deep Cox Mixtures for Survival Regression", Machine Learning for Healthcare Conference 2021
R material for LSHTM's Advanced Statistical Methods in Epidemiology (ASME) practical sessions
Resources for Survival Analysis
🍊 ➕ Survival Analysis add-on for Orange3 data mining suite.
snpnet - Efficient Lasso Solver for Large-scale genetic variant data
A 30+ node flowchart for selecting the right statistical test for evaluating experimental data.
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