Reproduce “An Adaptive Machine Learning Triage Framework for Predicting Alzheimer's Disease Progression” on OpenResearch

Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.

Universal app for Apple silicon and Intel. Requires macOS 11 or later.

About this paper

Richard Hou, Shengpu Tang, Wei Jin

The authors propose a triage system for two-year Alzheimer’s conversion prediction in mild cognitive impairment. It uses routine data first, then routes selected patients to advanced cerebrospinal-fluid and PET testing. On an ADNI test set of 100, it achieved AUROC 0.928 while escalating about 80%, versus 0.915 for the advanced model; the difference was not significant in this study.

Summary by alphaXiv.

Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.