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Article

Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning.

Journal of biomedical informatics - 1 May 2025

Zhou Manqi, Tang Alice S, Zhang Hao, Xu Zhenxing, Ke Alison M C, Su Chang, Huang Yu, Mantyh William G, Jaffee Michael S, Rankin Katherine P, DeKosky Steven T, Zhou Jiayu, Guo Yi, Bian Jiang, Sirota Marina, Wang Fei

Abstract excerpt

OBJECTIVE: Identification of clinically meaningful subphenotypes of disease progression can enhance the understanding of disease heterogeneity and underlying pathophysiology. In this study, we propose a machine learning framework to identify subphenotypes of Alzheimer's disease progression based on longitudinal real-world patient records. METHODS: The framework, dynaPhenoM, extracts coherent clinical topics...

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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Source

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Identifiers

PMID: 40180206
DOI: 10.1016/j.jbi.2025.104820
PMCID: PMC12296293

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