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Updated: Jan 19, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Using an Overlapping Time Interval Strategy to Study Diagnostic Instability in Mild Cognitive Impairment Subtypes
David Facal1, Joan Guàrdia-Olmos2, Arturo X Pereiro3
1Department of Developmental Psychology, University de Santiago de Compostela, 15782 Santiago de Compostela, Galicia, Spain. david.facal@usc.es.
Abstract:
(1) Background: Mild cognitive impairment (MCI) is a diagnostic label in which stability is typically low. The aim of this study was to examine temporal changes in the diagnosis of MCI subtypes by using an overlapping-time strategy; (2) Methods: The study included 435 participants aged over 50 years with subjective cognitive complaints and who completed at least one follow-up evaluation. The probability of transition was estimated using Bayesian odds ratios; (3) Results: Within the different time intervals, the controls with subjective cognitive complaints represented the largest proportion of participants, followed by sda-MCI at baseline and in the first five intervals of the follow-up, but not in the last eight intervals. The odds ratios indicated higher odds of conversion to dementia in sda-MCI and mda-MCI groups relative to na-MCI (e.g., interval 9-15 months-sda-MCI OR = 9 and mda-MCI OR = 3.36; interval 27-33-sda-MCI OR = 16 and mda-MCI = 5.06; interval 42-48-sda-MCI OR = 8.16 and mda-MCI = 3.45; interval 45-51-sda-MCI OR = 3.31 and mda-MCI = 1); (4) Conclusions: Notable patterns of instability consistent with the current literature were observed. The limitations of a prospective approach in the study of MCI transitions are discussed.
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