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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
CSIS: Proposal for a New Combined Screening Interpretation Score for Patients with Mild Cognitive Impairment
María Josefina Gonzalez Aguilar1, Lucía Alba Ferrara1,2
1Department of Psychology, Faculty of Biomedical Sciences, Austral University Buenos Aires, Argentina.
Abstract:
Introduction: It is essential to have sensitive, economical and quick cognitive screening tools for early detection of Mild Cognitive Impairment (MCI). The objective of the present study was to assess a new way of interpreting widely used screening tests, generating a new score: the CSIS (Combined Screening Interpretation Score). The CSIS considers the performance in various routine screening tests (MMSE, Clock drawing test, Short form of the Boston naming test, Phonological and Semantic fluency tests and the Frontal Assessment Battery) by summing up their gross scores in one general score. Methods: We calculated the CSIS of 90 Hispanic older adults without dementia (40 controls and 50 patients with a diagnosis of MCI). The differences of the CSIS between the groups, and the discriminative capacity of the CSIS and each separate test were analyzed. Results: Significant differences in the CSIS were observed between the groups, as a higher discriminative capacity of the CSIS compared to the other screening tests. A score of 86 points in the CSIS discriminates the groups with 84% sensitivity and 90% specificity. Conclusion: It is concluded that the CSIS is a useful, simple and brief tool to assess the cognitive performance of subjects with MCI.
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