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Improving the Sensitivity and Specificity of MCI Screening with Linguistic Information.

Conference paper
Authors Kathleen Fraser
Kristina Lundholm Fors
Marie Eckerström
Charalambos Themistocleous
Dimitrios Kokkinakis
Published in Proceedings of the LREC workshop: Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric impairments (RaPID-2). 8th of May 2018, Miyazaki, Japan / Dimitrios Kokkinakis (ed.)
ISBN 979-10-95546-26-9
Publication year 2018
Published at Department of Swedish
Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry
Centre for Ageing and Health (Agecap)
Language en
Links lrec-conf.org/workshops/lrec2018/W3...
Keywords language processing, machine learning, cognitive impairment, MMSE
Subject categories Language Technology (Computational Linguistics)

Abstract

The Mini-Mental State Exam (MMSE) is a screening tool for cognitive impairment. It has been extensively validated and is widely used, but has been criticized as not being effective in detecting mild cognitive impairment (MCI). In this study, we examine the utility of augmenting MMSE scores with automatically extracted linguistic information from a narrative speech task to better differentiate between individuals with MCI and healthy controls in a Swedish population. We find that with the addition of just four linguistic features, the F score (measuring a trade-off between sensitivity and specificity) is improved from 0.67 to 0.81 in logistic regression classification. These preliminary results suggest that the accuracy of traditional screening tools may be improved through the addition of computerized language analysis.

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