The research collects spoken descriptions of headache disorders by participants with headache disorders. The speech recordings are analyzed by natural language processing (NLP) tools to analyse linguistic properties of the texts and to obtain insight into the potential of NLP machine learning models for the recognition of headache syndromes of the participants.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
OTHER
Masking
NONE
Enrollment
2
Recordings will be transcribed manually to written digital text formats, on which NLP tools such as tokenisation and lexical analysis will be performed.
UZ Gent - Dienst Neurologie
Ghent, Belgium
Linguistic analysis of texts
Descriptive linguistic analysis of lexical choices, sentence formation and thematic content within the texts
Time frame: through study completion, an average of 1 year
Machine learning modelling for classification of headache disorders
To investigate the potential and learn insights of machine learning modelling to classify headache disorders based on the descriptions by participants
Time frame: through study completion, an average of 1 year
Machine learning modelling for estimation of headache impact scores
To investigate the potential and learn insights of machine learning modelling to estimate headache impact scores from different validated questionnaires investigating burden of disease and quality of life
Time frame: through study completion, an average of 1 year
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