A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python

Use of state of the art Convolutional neural network architectures including 3D UNet, 3D VNet and 2D UNets for Brain Tumor Segmentation and using segmented image features for Survival Prediction of patients through deep neural networks.

Extended Joint Models for Longitudinal and Survival Data

MachineShop: R package of models and tools for machine learning

Survival analysis in health economic evaluation Contains a suite of functions to systematise the workflow involving survival analysis in health economic evaluation. survHE can fit a large range of survival models using both a frequentist approach (by calling the R package flexsurv) and a Bayesian perspective.

Supplementary material for the paper: A review on competing risks methods for survival analysis

SCOR Datathon in 2020. Acquired and processed open data, predicted level of Glycohemoglobin, Cholesterol and probability of diabetes, then identified the probability change with Random Survival Forest to suggest improvements to a user.

Survival Analysis of Lung Cancer Patients

Survival functions for DataSHIELD. Package for building survival models, Cox proportional hazards models and Cox regression models in DataSHIELD.

A bookdown demonstrating how to build survival models using the dsSurvival package in DataSHIELD

A bookdown demonstrating how to build survival models using the dsSurvival package in DataSHIELD

Survival functions (client side) for DataSHIELD. Package for building survival models, Cox proportional hazards models and Cox regression models in DataSHIELD.

Survival Studio - a tool for automatic and interactive exploration of complex survival models. The project is carried out as part of the master's thesis supervised by Przemyslaw Biecek.

This repository holds the source materials used for a working group/symposium on capture-recapture models and social networks, and an introduction to capture-recapture models in nimble to estimate demographic parameters.

A repository of code and resources for survival models

Survival Analysis VS Machine Learning.

Identified the drivers of the risk of coronary heart disease and cardiovascular disease using the Sleep Heart Health Study dataset

A general model for the joint analysis of multivariate longitudinal data and survival time

Image-based Survival Modeling on simulated data

In this work I tested if there was an improvement in performance in the use of different survival models.