Longitudinal methods for Alzheimer's cognitive status prediction with deep learning.

ABSTRACT:

Introduction

Prediction of amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) using machine learning has primarily focused on short-term predictions spanning 1-3 years. This study aimed to develop a new machine learning technique to extend predictions of cognitive status over 3-10 years from their last visit.

Methods

We leveraged deep learning to analyze two longitudinal feature sets: (1) neuropsychological data and (2) neuropsychological data with the addition of patient history data. We also introduce two modeling techniques: (1) to separate normalized baseline features and deviations from baseline, and (2) a new linear attention-based imputation method.

Results

We demonstrate (1) our technique achieves high 1vA accuracy, representing 81.65% for

SUBMITTER: Liu H 

PROVIDER: S-EPMC12461166 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature