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

psfmi: Predictor Selection Functions for Logistic and Cox regression models in multiply imputed datasets

A 30+ node flowchart for selecting the right statistical test for evaluating experimental data.

Deep learning prediction on BRAF genetic mutation for patients with pediatric low grade glioma (pLGG).

Health app, that predicts diabetic retinopathy