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Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT05153876
Other study ID # BC-10635
Secondary ID
Status Completed
Phase
First received
Last updated
Start date October 11, 2021
Est. completion date January 1, 2024

Study information

Verified date May 2024
Source University Hospital, Ghent
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial 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.


Recruitment information / eligibility

Status Completed
Enrollment 1150
Est. completion date January 1, 2024
Est. primary completion date December 31, 2023
Accepts healthy volunteers No
Gender All
Age group 18 Years and older
Eligibility 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

Study Design


Intervention

Other:
Questionnaires
Headache attack descriptions, Headache related disability descriptions, Questionnaires, MIDAS, MSQv2.1, SF36

Locations

Country Name City State
Belgium University Hospital, Ghent: Department of Neurology Ghent

Sponsors (1)

Lead Sponsor Collaborator
University Hospital, Ghent

Country where clinical trial is conducted

Belgium, 

Outcome

Type Measure Description Time frame Safety issue
Primary Lexical diversity and differences between migraine and cluster headache Chi-square measurement of a word token used by migraine patients versus cluster headache patients through study completion, an average of 1 year
Secondary 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. through study completion, an average of 1 year
Secondary 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. through study completion, an average of 1 year
Secondary Word counts Counts of word tokens of different headache disorder groups through study completion, an average of 1 year
Secondary Sentences counts Counts of sentence tokens of different headache disorder groups through study completion, an average of 1 year
Secondary Paragraph counts Counts of paragraph tokens of different headache disorder groups through study completion, an average of 1 year
Secondary Term-frequency inverse document frequency scores (TF-IDF) TF-IDF scores of word tokens of different headache disorder groups through study completion, an average of 1 year
Secondary 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. through study completion, an average of 1 year
Secondary 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. through study completion, an average of 1 year
Secondary 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. through study completion, an average of 1 year
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