Predicting Dementia Screening and Staging Scores From Semantic Verbal Fluency Performance

Nicklas Linz 1 Johannes Tröger 1 Jan Alexandersson 1 Alexandra Konig 2, 3 Philippe Robert 2 Maria Wolters 4
1 DFKI - Deutsches Forschungszentrum für Künstliche Intelligenz GmbH
2 CobTek - Cognition Behaviour Technology
3 STARS - Spatio-Temporal Activity Recognition Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
4 University of Edinburgh
Abstract : 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 κ 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.
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Submitted on : Tuesday, December 26, 2017 - 12:26:45 PM
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Nicklas Linz, Johannes Tröger, Jan Alexandersson, Alexandra Konig, Philippe Robert, et al.. Predicting Dementia Screening and Staging Scores From Semantic Verbal Fluency Performance. ICDM 2017 - IEEE International Conference on Data Mining, Workshop on Data Mining for Aging, Rehabilitation and Independent Assisted Living, Nov 2017, New Orleans, United States. pp.719-728, ⟨10.1109/ICDMW.2017.100⟩. ⟨hal-01672590⟩

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