Data-driven identification of endophenotypes of Alzheimer’s disease progression: implications for clinical trials and therapeutic interventions

Data-driven identification of endophenotypes of Alzheimer’s disease progression: implications for clinical trials and therapeutic interventions

Abstract Background Given the complex and progressive nature of Alzheimer’s disease (AD), a precision medicine approach for diagnosis and treatment requires the identification of patient subgroups with biomedically distinct and actionable phenotype definitions. Methods Longitudinal patient-level dat...

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Bibliographic Details
Main Authors: Nophar Geifman, Richard E. Kennedy, Lon S. Schneider, Iain Buchan, Roberta Diaz Brinton
Format: Article
Language:English
Published: BMC 2018-01-01
Series:Alzheimer’s Research & Therapy
Subjects:
Alzheimer’s disease
Precision medicine
Endophenotypes
Machine learning
Statistical learning
Latent class mixed models
Online Access:http://link.springer.com/article/10.1186/s13195-017-0332-0

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http://link.springer.com/article/10.1186/s13195-017-0332-0

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