Cluster Headache, Headache Disorders, Migraine Disorders, Secondary Headache Disorder, TACS, Tension-Type Headache
Conditions
Keywords
Migraine, Natural Language Processing, Headache
Brief summary
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.
Interventions
OTHERQuestionnaires
Headache attack descriptions, Headache related disability descriptions, Questionnaires, MIDAS, MSQv2.1, SF36
Sponsors
University Hospital, Ghent
Study design
Observational model
ECOLOGIC_OR_COMMUNITY
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* 18 year or older * Have a headache disorder with at least one headache attack over the last three months * voluntary participation * accepted the patient information sheet and gave informed consent
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Lexical diversity and differences between migraine and cluster headache | through study completion, an average of 1 year | Chi-square measurement of a word token used by migraine patients versus cluster headache patients |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| F1 scores of machine learning experiments for the correct classification of headache disorders | through study completion, an average of 1 year | Machine learning experiments to investigate the potential to build modelling algorithms that accurately classify the self-given diagnosis by the patient based on text. |
| Word counts | through study completion, an average of 1 year | Counts of word tokens of different headache disorder groups |
| Sentences counts | through study completion, an average of 1 year | Counts of sentence tokens of different headache disorder groups |
| Paragraph counts | through study completion, an average of 1 year | Counts of paragraph tokens of different headache disorder groups |
| Accuracy of machine learning experiments for the correct classification of headache disorders | through study completion, an average of 1 year | Machine learning experiments to investigate the potential to build modelling algorithms that accurately classify the self-given diagnosis by the patient based on text. |
| Migraine Disabillity Assessment [MIDAS] score calculation with text input | through study completion, an average of 1 year | 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. |
| Migraine Specific Questionaire versie 2.1 [MSQv2.1] score calculation with text input | through study completion, an average of 1 year | 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. |
| RAND SF-36 Dutch version score calculation with text input | through study completion, an average of 1 year | 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. |
| Term-frequency inverse document frequency scores (TF-IDF) | through study completion, an average of 1 year | TF-IDF scores of word tokens of different headache disorder groups |
Countries
Belgium
Outcome results
None listed