Learning the progression and clinical subtypes of Alzheimer's disease from longitudinal clinical data

TL;DR

This paper employs machine learning techniques on longitudinal clinical data to identify Alzheimer's disease subtypes and predict disease progression, aiming to improve personalized care and early diagnosis.

Contribution

It introduces a novel application of unsupervised and supervised machine learning to classify Alzheimer's subtypes and forecast progression using ADNI data.

Findings

Progression space divided into low, moderate, high zones

Models enable early detection of disease subtypes

Potential to improve personalized treatment planning

Abstract

Alzheimer's disease (AD) is a degenerative brain disease impairing a person's ability to perform day to day activities. The clinical manifestations of Alzheimer's disease are characterized by heterogeneity in age, disease span, progression rate, impairment of memory and cognitive abilities. Due to these variabilities, personalized care and treatment planning, as well as patient counseling about their individual progression is limited. Recent developments in machine learning to detect hidden patterns in complex, multi-dimensional datasets provides significant opportunities to address this critical need. In this work, we use unsupervised and supervised machine learning approaches for subtype identification and prediction. We apply machine learning methods to the extensive clinical observations available at the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set to identify patient subtypes and to predict disease progression. Our analysis depicts the progression space for the Alzheimer's disease into low, moderate and high disease progression zones. The proposed work will enable early detection and characterization of distinct disease subtypes based on clinical heterogeneity. We anticipate that our models will enable patient counseling, clinical trial design, and ultimately individualized clinical care.