Predicting Dementia Screening and Staging Scores From Semantic Verbal Fluency Performance

Nicklas Linz, Johannes Tröger, Jan Alexandersson, Maria Wolters, Alexandra König, Philippe Robert

In: 2017 IEEE 17th International Conference on Data Mining Workshops (ICDMW). Workshop on Data Mining for Ageing, Rehabilitation and Independent Assisted Living (ARIAL-17) befindet sich 17th November 18-21 New Orleans LA United States IEEE Computer Society 2017.


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.

PID5010365.pdf (pdf, 902 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence