Learning the progression and clinical subtypes of Alzheimer's disease from longitudinal clinical data
This work addresses the need for personalized care and treatment planning in Alzheimer's disease by enabling early detection and characterization of distinct disease subtypes based on clinical heterogeneity.
The researchers tackled the problem of heterogeneous progression in Alzheimer's disease by using machine learning on longitudinal clinical data from ADNI to identify patient subtypes and predict disease progression, categorizing it into low, moderate, and high progression zones.
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.