Modeling and prediction of clinical symptom trajectories in Alzheimer's disease using longitudinal data.

ABSTRACT: Computational models predicting symptomatic progression at the individual level can be highly beneficial for early intervention and treatment planning for Alzheimer's disease (AD). Individual prognosis is complicated by many factors including the definition of the prediction objective itself. In this work, we present a computational framework comprising machine-learning techniques for 1) modeling symptom trajectories and 2) prediction of symptom trajectories using multimodal and longitudinal data. We perform primary analyses on three cohorts from Alzheimer's Disease Neuroimaging Initiative (ADNI), and a replication analysis using subjects from Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL). We model the prototypical symptom trajectory classes using clinical asses

SUBMITTER: Bhagwat N 

PROVIDER: S-EPMC6157905 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature