Feasibility Study of a Machine Learning Approach to Predict Dementia Progression

Abstract

We conducted a feasibility study of machine-learning to predict progression of cognitive impairment to Alzheimer's disease (AD) among individuals enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our approach uses diverse participant information including genetic, imaging, biomarker, and neuropsychological data to predict transition to dementia in three clinical scenarios: short-term prediction (half or one year) based on a single assessment (simulating a "new patient" visit), short-term prediction based on information from two time points (simulating a "follow up" visit), and long-term (multiple years) prediction (simulating ongoing follow-up with repeated opportunities for assessment).

Ähnliche Arbeiten