Predicting Dementia Screening and Staging Scores From Semantic Verbal Fluency Performance

Nicklas Linz, Johannes Troeger, Jan Alexandersson, Alexandra Koenig, Philippe Robert, Maria Wolters

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

The standard dementia screening tool Mini Mental State Examination (MMSE) and the standard dementia staging tool Clinical Dementia Rating Scale (CDR) are prominent methods for answering questions whether a person might have dementia and about the dementia severity respectively. These methods are time consuming and require well-educated personnel to administer. Conversely, cognitive tests, such as the Semantic Verbal Fluency (SVF), demand little time. With this as a starting point, we investigate the relation between SVF results and MMSE/CDR-SOB scores. We use regression models to predict scores based on persons’ SVF performance. Over a set of 179 patients with different degree of dementia, we achieve a mean absolute error of of 2.2 for MMSE (range 0–30) and 1.7 for CDR-SOB (range 0–18). True and predicted scores agree with a Cohen’s of 0.76 for MMSE and 0.52 for CDR-SOB. We conclude that our approach has potential to serve as a cheap dementia screening, possibly even in non-clinical settings.
Original languageEnglish
Title of host publicationFirst Workshop on Data Mining for Aging, Rehabilitation and Independent Assisted Living
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages10
ISBN (Electronic)978-1-5386-3800-2
ISBN (Print)978-1-5386-3801-9
Publication statusPublished - 18 Dec 2017
Event2017 IEEE International Conference on Data Mining Workshops - New Orleans, United States
Duration: 18 Nov 201721 Nov 2017

Publication series

ISSN (Electronic)2375-9259


Conference2017 IEEE International Conference on Data Mining Workshops
Abbreviated titleICDM 2017
Country/TerritoryUnited States
CityNew Orleans
Internet address


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