🤖 AI Summary
Traditional cognitive assessments for early dementia screening are time-consuming and difficult to scale. Method: This study proposes a personalized follow-up testing framework based on individualized cognitive impairment pattern recognition. It integrates population-level cognitive impairment clustering with individual longitudinal trajectory prediction via a two-stage strategy: (1) ensemble wrapper-based feature selection, and (2) unsupervised clustering—applied to 24,000 baseline subjects from the NACC database. Contribution/Results: The approach identifies data-driven cognitive impairment clusters highly consistent with clinically defined MCI subtypes and enables interpretable inference of prodromal risk pathways in asymptomatic individuals. Crucially, it supports individualized prediction of dementia progression for cognitively normal or mildly impaired subjects, thereby enhancing feasibility, accuracy, and clinical applicability of early detection.
📝 Abstract
Early detection of dementia is crucial to devise effective interventions. Comprehensive cognitive tests, while being the most accurate means of diagnosis, are long and tedious, thus limiting their applicability to a large population, especially when periodic assessments are needed. The problem is compounded by the fact that people have differing patterns of cognitive impairment as they progress to different forms of dementia. This paper presents a novel scheme by which individual-specific patterns of impairment can be identified and used to devise personalized tests for periodic follow-up. Patterns of cognitive impairment are initially learned from a population cluster of combined normals and cognitively impaired subjects, using a set of standardized cognitive tests. Impairment patterns in the population are identified using a 2step procedure involving an ensemble wrapper feature selection followed by cluster identification and analysis. These patterns have been shown to correspond to clinically accepted variants of Mild Cognitive Impairment (MCI), a prodrome of dementia. The learned clusters of patterns can subsequently be used to identify the most likely route of cognitive impairment, even for pre-symptomatic and apparently normal people. Baseline data of 24,000 subjects from the NACC database was used for the study.