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Updated: Apr 18, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Mild cognitive impairment (MCI) in long-term care patients: subtype classification and occurrence
William E Mansbach1, Ryan A Mace1, Kristen M Clark1
1a Mansbach Health Tools, LLC , Simpsonville , MD , USA.
Objectives:
This study examines mild cognitive impairment (MCI) in long-term care settings by identifying and quantifying MCI subtypes in a combined sample of nursing home and assisted-living patients. We compared impairment thresholds of 1-SD and 1.5-SD to determine if different cut-offs differentially affect occurrence rates.
Method:
One hundred and eight participants who met general criteria for MCI were included for the purposes of this study. The general diagnosis of MCI was based on criteria. Participants were further grouped into MCI subtypes. Based on previously established norms, Brief Cognitive Assessment Tool (BCAT) factor scores were used to assess whether MCI participants met either the 1-SD and 1.5-SD impairment thresholds for memory, executive functions, and attentional capacity.
Results:
Using both 1-SD and 1.5-SD impairment thresholds, three clear MCI subtypes were identified: amnestic, single-domain; non-amnestic, single-domain (executive); and amnestic, multi-domain (memory and executive). A fourth category (undifferentiated) was identified in patients who did not meet criteria for a distinct MCI subtype, but still had cognitive impairments. The stricter impairment threshold of 1.5-SD resulted in fewer patients classified as having any of the three domain-specific subtypes.
Conclusion:
Based on a sample of nursing home and assisted-living patients, we identified three MCI subtypes, and a fourth category consisting of participants with general MCI, but without clear evidence of domain-specific cognitive impairment. When selecting impairment thresholds, one should consider the impact on the identification of MCI subtypes and the probability of misdiagnoses.
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