A Random Survival Forest implementation for python inspired by Ishwaran et al. - Easily understandable, adaptable and extendable.

geneSurv: an interactive web-based tool for survival analysis in genomics research

Building Explanations through a LocaLly AccuraTe Rule EXtractor

Federated Learning with Random Survival Forests.

Survival learners for the `mlexperiments` R 📦

Predicting customers' churn with survival analysis, including time-dependent variables

An interactive Streamlit dashboard for analyzing equipment failure patterns and predicting maintenance needs. Features include data visualization, root cause analysis using Apriori algorithm, and survival modeling with Random Survival Forests to estimate time-to-failure and optimize spare parts management.

Survival analysis of stomach cancer, using TCGA gene expression data.

Statistical models and machine learning models on health care data

Survival analysis of diabetic patient readmission using Cox Proportional Hazards and Random Survival Forests, focusing on time-to-event modeling, risk stratification, and clinical interpretability.

Risk assessment via survival modelling

First Author undergrad research work supervised by Dr. Samuel Wong at University of Waterloo in the prediction performance of functional survival models.

reproducibility results for IAL2023 workshop

Análise de sobrevivência aplicada a um conjunto de dados de câncer de mama, incluindo informações sobre terapia hormonal, quimioterapia, radioterapia e variáveis de tempo até o evento. O estudo utiliza curvas de Kaplan–Meier, modelos GLM e Random Survival Forest.