Identification of high likelihood of dementia in population-based surveys using unsupervised clustering: a longitudinal analysis

• Published in: Alzheimer's research & therapy Vol. 15; no. 1; p. 209
• Main Authors: Gharbi-Meliani, Amin, Husson, François, Vandendriessche, Henri, Bayen, Eleonore, Yaffe, Kristine, Bachoud-Lévi, Anne-Catherine, Cleret de Langavant, Laurent
• Format: Journal Article
• Language: English
• Published: England BioMed Central 29.11.2023; BMC
• Subjects: Age; Aged; Aging; Aging - psychology; Air pollution; Alcohol; Alzheimer's disease; Blood pressure; Body mass index; Clustering; Cognition; Cognition & reasoning; Cognitive Dysfunction - diagnosis; Dementia; Dementia - diagnosis; Dementia - epidemiology; Diabetes; Female; Hearing loss; Humans; Hypertension; Identification; Life Sciences; Longitudinal analysis; Longitudinal Studies; Machine Learning; Male; Methods; Middle Aged; Neurons and Cognition; Population; Population-based surveys; Risk factors; Self report; Social isolation; Statistics; Unsupervised clustering; Variables; Europe; Israel; Longitudinal analysis; Identification; Population-based surveys; Multistate models; Machine Learning; Dementia; Unsupervised clustering

Dementia is defined as a cognitive decline that affects functional status. Longitudinal ageing surveys often lack a clinical diagnosis of dementia though measure cognition and daily function over time. We used unsupervised machine learning and longitudinal data to identify transition to probable dem...