Predicting Dementia Screening and Staging Scores From Semantic Verbal Fluency Performance

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

doi:10.1109/ICDMW.2017.100

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 proceeding › Conference contribution

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 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 language	English
Title of host publication	First Workshop on Data Mining for Aging, Rehabilitation and Independent Assisted Living
Publisher	Institute of Electrical and Electronics Engineers
Number of pages	10
ISBN (Electronic)	978-1-5386-3800-2
ISBN (Print)	978-1-5386-3801-9
DOIs	https://doi.org/10.1109/ICDMW.2017.100
Publication status	Published - 18 Dec 2017
Event	2017 IEEE International Conference on Data Mining Workshops - New Orleans, United States Duration: 18 Nov 2017 → 21 Nov 2017 http://icdm2017.bigke.org/

Publication series

Name
Publisher	IEEE
ISSN (Electronic)	2375-9259

Conference

Conference	2017 IEEE International Conference on Data Mining Workshops
Abbreviated title	ICDM 2017
Country/Territory	United States
City	New Orleans
Period	18/11/17 → 21/11/17
Internet address	http://icdm2017.bigke.org/

Access to Document

10.1109/ICDMW.2017.100

PID5010365-1Accepted author manuscript, 902 KB

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8215732

Element- Early detection of cognitive disorders such as dementia on the basis of speech analysis
Wolters, M. (Principal Investigator)
EU government bodies
1/01/17 → 31/12/17
Project: Research

Cite this

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title = "Predicting Dementia Screening and Staging Scores From Semantic Verbal Fluency Performance",

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{\textquoteright} 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{\textquoteright}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.",

author = "Nicklas Linz and Johannes Troeger and Jan Alexandersson and Alexandra Koenig and Philippe Robert and Maria Wolters",

year = "2017",

month = dec,

day = "18",

doi = "10.1109/ICDMW.2017.100",

language = "English",

isbn = "978-1-5386-3801-9",

publisher = "Institute of Electrical and Electronics Engineers",

booktitle = "First Workshop on Data Mining for Aging, Rehabilitation and Independent Assisted Living",

address = "United States",

note = "2017 IEEE International Conference on Data Mining Workshops, ICDM 2017 ; Conference date: 18-11-2017 Through 21-11-2017",

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Linz, N, Troeger, J, Alexandersson, J, Koenig, A, Robert, P & Wolters, M 2017, Predicting Dementia Screening and Staging Scores From Semantic Verbal Fluency Performance. in First Workshop on Data Mining for Aging, Rehabilitation and Independent Assisted Living. Institute of Electrical and Electronics Engineers, 2017 IEEE International Conference on Data Mining Workshops, New Orleans, Louisiana, United States, 18/11/17. https://doi.org/10.1109/ICDMW.2017.100

Predicting Dementia Screening and Staging Scores From Semantic Verbal Fluency Performance. / Linz, Nicklas ; Troeger, Johannes; Alexandersson, Jan et al.
First Workshop on Data Mining for Aging, Rehabilitation and Independent Assisted Living. Institute of Electrical and Electronics Engineers, 2017.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Predicting Dementia Screening and Staging Scores From Semantic Verbal Fluency Performance

AU - Linz, Nicklas

AU - Troeger, Johannes

AU - Alexandersson, Jan

AU - Koenig, Alexandra

AU - Robert, Philippe

AU - Wolters, Maria

PY - 2017/12/18

Y1 - 2017/12/18

N2 - 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.

AB - 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.

U2 - 10.1109/ICDMW.2017.100

DO - 10.1109/ICDMW.2017.100

M3 - Conference contribution

SN - 978-1-5386-3801-9

BT - First Workshop on Data Mining for Aging, Rehabilitation and Independent Assisted Living

PB - Institute of Electrical and Electronics Engineers

T2 - 2017 IEEE International Conference on Data Mining Workshops

Y2 - 18 November 2017 through 21 November 2017

ER -

Predicting Dementia Screening and Staging Scores From Semantic Verbal Fluency Performance

Abstract

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Conference

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Element- Early detection of cognitive disorders such as dementia on the basis of speech analysis

Cite this