Identifying Alzheimer's Disease Progression Subphenotypes Via a Graph-based Framework Using Electronic Health Records

Authors

• Yu Huang
• Jie Xu
• Zhengkang Fan
• Yu Hu
• Xing He
• Aokun Chen
• Yuxi Liu
• Rui Yin
• Jingchuan Guo
• Steven T DeKosky
• Michael Jaffee
• Manqi Zhou
• Chang Su
• Fei Wang
• Yi Guo
• Jiang Bian

Language

English

Publication Date

2-11-2026

Journal

Journal of Healthcare Informatics Research

DOI

10.1007/s41666-026-00230-2

PMID

42063485

PMCID

PMC13128099

PubMedCentral® Posted Date

2-11-2026

PubMedCentral® Full Text Version

Post-print

Abstract

Understanding the heterogeneity of neurodegeneration in Alzheimer's disease (AD) and identifying distinct progression pathways is critical for improving diagnosis, treatment, prognosis, and prevention. Motivated by this need, this study aimed to identify disease progression subphenotypes among patients with mild cognitive impairment (MCI) and AD using electronic health records (EHRs). We developed a novel approach that combines a graph neural network (GNN)-based framework with time series clustering to characterize progression subphenotypes from MCI to AD. We applied the proposed framework to a real-world cohort of 2,525 patients (61.66% female; mean age 76 years), of whom 64.83% were Non-Hispanic White, 16.48% Non-Hispanic Black, 2.53% were of other races, and 10.85% were Hispanic. Our model identified four distinct progression subphenotypes, each exhibiting characteristic clinical patterns, with average MCI-to-AD progression times ranging from 805 to 1,236 days. These findings indicate that AD does not follow a uniform progression trajectory but instead manifests heterogeneous pathways, and the proposed framework provides an explainable, data-driven approach for delineating AD progression subphenotypes, offering actionable insights for healthcare informatics research and the clinical management of patients at risk for AD.

Keywords

Alzheimer’s disease, Disease progression subphenotyping, Real-world data, Graph neural network, Electronic health records

Published Open-Access

yes

Recommended Citation

Huang, Yu; Xu, Jie; Fan, Zhengkang; et al., "Identifying Alzheimer's Disease Progression Subphenotypes Via a Graph-based Framework Using Electronic Health Records" (2026). Faculty, Staff and Student Publications. 880.
https://digitalcommons.library.tmc.edu/uthshis_docs/880