Headache disorders are among the most prevalent medical conditions worldwide. The diagnosis of headache disorders is based on medical history taking. Digital solutions such as natural language processing (NLP) may be of aid to understand the linguistic aspects of headache attack and headache related disability descriptions by patients. Participants will provide a written description of their headache disorder. The results will hopefully lead to a better understanding of the potential use of NLP in headache disorders.
Study Type
OBSERVATIONAL
Enrollment
1,150
Headache attack descriptions, Headache related disability descriptions, Questionnaires, MIDAS, MSQv2.1, SF36
University Hospital, Ghent: Department of Neurology
Ghent, Belgium
Lexical diversity and differences between migraine and cluster headache
Chi-square measurement of a word token used by migraine patients versus cluster headache patients
Time frame: through study completion, an average of 1 year
Accuracy of machine learning experiments for the correct classification of headache disorders
Machine learning experiments to investigate the potential to build modelling algorithms that accurately classify the self-given diagnosis by the patient based on text.
Time frame: through study completion, an average of 1 year
F1 scores of machine learning experiments for the correct classification of headache disorders
Machine learning experiments to investigate the potential to build modelling algorithms that accurately classify the self-given diagnosis by the patient based on text.
Time frame: through study completion, an average of 1 year
Word counts
Counts of word tokens of different headache disorder groups
Time frame: through study completion, an average of 1 year
Sentences counts
Counts of sentence tokens of different headache disorder groups
Time frame: through study completion, an average of 1 year
Paragraph counts
Counts of paragraph tokens of different headache disorder groups
Time frame: through study completion, an average of 1 year
Term-frequency inverse document frequency scores (TF-IDF)
TF-IDF scores of word tokens of different headache disorder groups
Time frame: through study completion, an average of 1 year
Migraine Disabillity Assessment [MIDAS] score calculation with text input
Machine learning experiments to investigate the potential to build modelling algorithms that accurately predict the impact score from Migraine Disabillity Assessment \[MIDAS\] based on text.
Time frame: through study completion, an average of 1 year
Migraine Specific Questionaire versie 2.1 [MSQv2.1] score calculation with text input
Machine learning experiments to investigate the potential to build modelling algorithms that accurately predict the impact score from Migraine Specific Questionaire versie 2.1 \[MSQv2.1\] based on text.
Time frame: through study completion, an average of 1 year
RAND SF-36 Dutch version score calculation with text input
Machine learning experiments to investigate the potential to build modelling algorithms that accurately predict the impact score from RAND SF-36 Dutch version based on text.
Time frame: through study completion, an average of 1 year
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