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Updated: Sep 18, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Estimation of events in cohort studies based on probability of cognitive impairment
Jiyeon Park1, Erin L Abner2,3,4, Pei Wang5
1Department of Statistics, University of Kentucky, Lexington, KY, USA.
Abstract:
BackgroundDementia and Alzheimer's disease-causing pathologies progress slowly over decades, and participants are recruited cognitively intact, so designing studies to observe enough cases within a feasible timeframe is important.ObjectiveIn this study, we used readily available basic predictors, age, family history, sex, and apolipoprotein E (APOE) 4 allele carriership, to generate cumulative incidence functions for serious cognitive impairments over years of follow-up.MethodsThe data were taken from the University of Kentucky Alzheimer's Disease Research Center longitudinal cohort established in 1989. The participants were recruited cognitively unimpaired and aged 60+. The probability of serious cognitive impairment was assessed using a multinomial logistic model, with age, the number of risk factors (family history and APOE4 allele) and sex as predictors.ResultsWe estimated that when two or more risk factors are present, the long-term incidence of clinical mild cognitive impairment and dementia is 2.3 to 2.7 times higher than that of the 0-risk group for both sexes, whereas the 0-risk group experienced approximately 7.9% to 11.6% longer observation times for female and 0.9% to 4.8% for male compared to the two or more risks group.ConclusionsThis study presents the expected cumulative incidence functions over varying follow-up times, and the expected observation time of serious cognitive impairment for given family history, carriership of APOE4, age and sex.
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