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

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 Control, 72.87% for aMCI, and 86.52% for AD on a 3- to 10-year horizon, and (2) the new method is more accurate than previously proposed approaches for this time horizon.

DISCUSSION

This work offers a new set of techniques for big-data analysis of longitudinal dementia data.

Highlights

• Develops a new method for the prediction using deep learning of longitudinally verified amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) using the National Alzheimer's Coordinating Center NACC) database.
• Demonstrates comparable performance on the 3- to 10-year prediction horizon, which is significantly more challenging to predict than using the previous approach that only used a 1- to 3-year prediction horizon.
• Highlights that even the prediction of verified 3- to 10-year aMCI that eventually leads to AD is still a challenging task.