Mining Alzheimer's disease clinical data: reducing effects of natural aging for predicting progression and identifying subtypes.

ABSTRACT:

Introduction

Because Alzheimer's disease (AD) has significant heterogeneity in encephalatrophy and clinical manifestations, AD research faces two critical challenges: eliminating the impact of natural aging and extracting valuable clinical data for patients with AD.

Methods

This study attempted to address these challenges by developing a novel machine-learning model called tensorized contrastive principal component analysis (T-cPCA). The objectives of this study were to predict AD progression and identify clinical subtypes while minimizing the influence of natural aging.

Results

We leveraged a clinical variable space of 872 features, including almost all AD clinical examinations, which is the most comprehensive AD feature description in current research. T-cPCA yielde

SUBMITTER: Han T 

PROVIDER: S-EPMC11351280 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature