Project description: Alzheimer’s disease and related dementias affect more than 50 million people worldwide. No current treatments are available that can provably cure or even slow the progression of Alzheimer’s disease — all clinical trials have so far failed to prove a disease-modifying effect. One reason why they fail is the difficulty in identifying patients at early disease stages, when treatments are most likely to have an effect. The Alzheimer’s Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge was designed to find the best approaches for predicting disease progression and thus help with early identification of at-risk subjects.
This project will be run as a live 3-day competition, where participants will group into teams and create algorithms to predict the future in patients and those at risk of Alzheimer’s disease using a publicly-available dataset. We will run a live Kaggle-style leader board where participants will make predictions and see their performance results in real time. Prizes may be offered to the teams making the best predictions.
Associated resources:
https://arxiv.org/abs/1805.03909
https://tadpole.grand-challenge.org/
Pre-requisites:
Python