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Updated: May 21, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Evaluation of functional autonomy and neuropsychiatric symptoms in dementia and Mild Cognitive Impairment: a
Irene Cappadona1, Francesco Corallo1, Maria Pagano1
1IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy.
Background:
Neurocognitive disorders involve progressive cognitive decline, reduced functional autonomy, and frequent emotional or neuropsychiatric symptoms. Their variable progression highlights the need to identify factors linked to faster deterioration.
Aim:
This study aims to longitudinally examine functional autonomy, cognitive performance, and neuropsychiatric symptoms in individuals with mild cognitive impairment (MCI) and dementia to support more targeted and personalized interventions.
Materials And Methods:
This retrospective, single-center observational study includes patients who completed three neuropsychological evaluations approximately 12 months apart (T0, T1, T2). The dataset is expected to include at least 151participants . Data include demographic and diagnostic information, cognitive tests (MMSE, MoCA, MODA, FAB, CDT), functional measures (ADL, IADL), and emotional-behavioral scales (BDI, BAI, GDS, HDR-S, HRSA, NPI). Analyses involve descriptive statistics, group comparisons, longitudinal models, regression analyses, and propensity score methods.
Results:
Dementia patients are expected to show greater functional decline than those with MCI, although some may maintain autonomy. Emotional-behavioral symptoms are anticipated to correlate with accelerated deterioration.
Conclusions:
This protocol offers a multidimensional framework for characterizing decline in neurocognitive disorders and identifying early predictors of progression, supporting more tailored monitoring and intervention strategies.
Clinical Trial Registration:
www.clinicaltrials.gov identifier is NCT07287410.
