Lexical and syntactic deficits analyzed via automated natural language processing: the new monitoring tool in multiple sclerosis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064165%3A_____%2F23%3A10466081" target="_blank" >RIV/00064165:_____/23:10466081 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21230/23:00366987 RIV/00216208:11110/23:10466081

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=KrlwPYtflE" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=KrlwPYtflE</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1177/17562864231180719" target="_blank" >10.1177/17562864231180719</a>

Result language

angličtina

Original language name

Lexical and syntactic deficits analyzed via automated natural language processing: the new monitoring tool in multiple sclerosis

Original language description

Background: Impairment of higher language functions associated with natural spontaneous speech in multiple sclerosis (MS) remains underexplored. Objectives: We presented a fully automated method for discriminating MS patients from healthy controls based on lexical and syntactic linguistic features. Methods: We enrolled 120 MS individuals with Expanded Disability Status Scale ranging from 1 to 6.5 and 120 age-, sex-, and education-matched healthy controls. Linguistic analysis was performed with fully automated methods based on automatic speech recognition and natural language processing techniques using eight lexical and syntactic features acquired from the spontaneous discourse. Fully automated annotations were compared with human annotations. Results: Compared with healthy controls, lexical impairment in MS consisted of an increase in content words (p = 0.037), a decrease in function words (p = 0.007), and overuse of verbs at the expense of noun (p = 0.047), while syntactic impairment manifested as shorter utterance length (p = 0.002), and low number of coordinate clause (p &lt; 0.001). A fully automated language analysis approach enabled discrimination between MS and controls with an area under the curve of 0.70. A significant relationship was detected between shorter utterance length and lower symbol digit modalities test score (r = 0.25, p = 0.008). Strong associations between a majority of automatically and manually computed features were observed (r &gt; 0.88, p &lt; 0.001). Conclusion: Automated discourse analysis has the potential to provide an easy-to-implement and low-cost language-based biomarker of cognitive decline in MS for future clinical trials.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30103 - Neurosciences (including psychophysiology)

Project

<a href="/en/project/LX22NPO5107" target="_blank" >LX22NPO5107: National institute for Neurological Research</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Therapeutic Advances in Neurological Disorders

ISSN

1756-2856

e-ISSN

1756-2864

Volume of the periodical

16

Issue of the periodical within the volume

June

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

17562864231180719

UT code for WoS article

001013115100001

EID of the result in the Scopus database

2-s2.0-85163658732