An unsupervised learning approach for clustering joint trajectories of Alzheimer's disease biomarkers: An application to ADNI Data.

ABSTRACT:

Introduction

Current models of Alzheimer's disease (AD) progression assume a common pattern and pathology, oversimplifying the heterogeneity of clinical AD.

Methods

We define a syndrome as a unique biomarker progression pattern and develop a lag measure to cluster pre-dementia individuals, reflecting their pathology's multi-dimensionality. The technique uses the time-ordering of events to group individuals based on their position along the disease process and the relative positions of their markers.

Results

An application using Alzheimer's Disease Neuroimaging Initiative (ADNI) data highlights the need for our novel approach to clustering individuals into syndrome groups.

Discussion

Accurately characterizing biomarker curves associated with brain damage require

SUBMITTER: Sonmez TF 

PROVIDER: S-EPMC11851129 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature