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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Predicting Scores on the Mini-Mental State Examination (MMSE) from Spontaneous Speech
Alma M Bueno-Cayo1, Minerva Del Rio Carmona2, Rosa Castell-Enguix2
1Escuela de Doctorado, Universidad Católica de Valencia San Vicente Mártir, San Agustín 3, Esc. A, Entresuelo 1, 46002 Valencia, Spain.
Abstract:
The aim of this study was to examine the relationship between language components, such as lexical density, length, and content in terms of "Time, Space and Action", with MMSE scores. For this reason, a group of 33 older participants, without a diagnosis of dementia, was examined, providing information regarding recent and future events. Participants with higher MMSE scores showed higher lexical density, speech length, as well as number of tokens related to Time, Place and Action in their speech. However, these differences only reach the statistical level for lexical density when participants were divided into two groups (MCI and healthy controls). Word frequency was lower for participants with MCI but this difference was not statistically significant. Lastly, lexical density was positively correlated with MMSE scores and predicted MMSE scores. These results could be of interest at the applied level in the screening of MCI.
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