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Updated: Sep 13, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Developing a Patient Profile for the Detection of Cognitive Decline in Subjective Memory Complaint Patients: A
María Gil-Peinado1,2, Francisco Javier Muñoz-Almaraz1,3, Hernán Ramos1,2
1Cátedra DeCo MICOF-CEU UCH, Universidad Cardenal Herrera-CEU, 46115 Valencia, Spain.
Background And Objectives:
Early detection of cognitive decline (CD) is crucial for managing dementia risk factors and preventing disease progression. This study pursues two main objectives: (1) to review existing cognitive screening practices implemented in community pharmacy settings and (2) to characterize the cognitive profile of individuals eligible for screening in this context.
Materials And Methods:
This study was conducted in two phases. First, a scoping review of cognitive screening tools used in community pharmacies was carried out following PRISMA-ScR guidelines. Second, a cross-sectional study was performed to design and implement a CD screening protocol, assessing cognitive function. Data collection included demographic and clinical variables commonly associated with dementia risk. Decision tree analysis was applied to identify key variables contributing to the cognitive profile of patients eligible for screening.
Results:
The scoping review revealed that screening approaches differed by country and population, with limited pharmacy involvement suggesting implementation barriers. Cognitive screening was conducted in 18 pharmacies in Valencia, Spain (1.45%), involving 286 regular users reporting Subjective Memory Complaints (SMC). The average age of participants was 71 years, and 74.8% were women. According to the unbiased Gini impurity index, the most relevant predictors of CD-based on the corrected mean decrease in corrected impurity (MDcI), a bias-adjusted measure of variable importance-were age (MDcI: 2.60), internet and social media use (MDcI: 2.43), sleep patterns (MDcI: 1.83), and educational attainment (MDcI: 0.96). Simple decision trees can reduce the need for full screening by 53.6% while maintaining an average sensitivity of 0.707. These factors are essential for defining the profile of individuals who would benefit most from CD screening services.
Conclusions:
Community pharmacy-based detection of CD shows potential, though its implementation remains limited by issues of consistency and feasibility. Enhancing early dementia detection in primary care settings may be achieved by prioritizing individuals with limited internet and social media use, irregular sleep patterns, and lower education levels. Targeting these groups could significantly improve the effectiveness of CD screening programs.
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