Examining heterogeneity in dementia using data-driven unsupervised clustering of cognitive profiles.

ABSTRACT: Dementia is characterized by a decline in memory and thinking that is significant enough to impair function in activities of daily living. Patients seen in dementia specialty clinics are highly heterogenous with a variety of different symptoms that progress at different rates. Recent research has focused on finding data-driven subtypes for revealing new insights into dementia's underlying heterogeneity, rather than assuming that the cohort is homogenous. However, current studies on dementia subtyping have the following limitations: (i) focusing on AD-related dementia only and not examining heterogeneity within dementia as a whole, (ii) using only cross-sectional baseline visit information for clustering and (iii) predominantly relying on expensive imaging biomarkers as features for cluster

SUBMITTER: Kumar S 

PROVIDER: S-EPMC11563363 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature