Published November 18, 2017
| Version v1
Conference paper
Predicting Dementia Screening and Staging Scores From Semantic Verbal Fluency Performance
Contributors
Others:
- Deutsches Forschungszentrum für Künstliche Intelligenz GmbH = German Research Center for Artificial Intelligence (DFKI)
- Cognition Behaviour Technology (CobTek) ; Université Nice Sophia Antipolis (1965 - 2019) (UNS) ; COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre Hospitalier Universitaire de Nice (CHU Nice)-Institut Claude Pompidou [Nice] (ICP - Nice)-Université Côte d'Azur (UCA)
- Spatio-Temporal Activity Recognition Systems (STARS) ; Inria Sophia Antipolis - Méditerranée (CRISAM) ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
- University of Edinburgh
Description
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.
Abstract
International audienceAdditional details
Identifiers
- URL
- https://hal.inria.fr/hal-01672590
- URN
- urn:oai:HAL:hal-01672590v1
Origin repository
- Origin repository
- UNICA