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NCT Number: NCT05153876

An Open Internet-based Survey and Natural Language Processing Project Analysing Written Monologues by Headache Patients

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.

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Key information

Conditions

Headache Disorders Brain Diseases Central Nervous System Diseases Cluster Headache Headache Headache Disorders, Primary Headache Disorders, Secondary Migraine Disorders Nervous System Diseases Neurologic Manifestations Pain Pathological Conditions, Signs and Symptoms Secondary Headache Disorder Signs and Symptoms Tension-Type Headache Trigeminal Autonomic Cephalalgias tACS

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

University Hospital, Ghent: Department of Neurology

Ghent, 9000, Belgium

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

questionnaires

Other

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

Primary outcomes

  1. Lexical diversity and differences between migraine and cluster headache

    Time frame: through study completion, an average of 1 year

    Chi-square measurement of a word token used by migraine patients versus cluster headache patients

Secondary outcomes

  1. Accuracy of machine learning experiments for the correct classification of headache disorders

    Time frame: 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.

  2. F1 scores of machine learning experiments for the correct classification of headache disorders

    Time frame: 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.

  3. Word counts

    Time frame: through study completion, an average of 1 year

    Counts of word tokens of different headache disorder groups

  4. Sentences counts

    Time frame: through study completion, an average of 1 year

    Counts of sentence tokens of different headache disorder groups

  5. Paragraph counts

    Time frame: through study completion, an average of 1 year

    Counts of paragraph tokens of different headache disorder groups

  6. Term-frequency inverse document frequency scores (TF-IDF)

    Time frame: through study completion, an average of 1 year

    TF-IDF scores of word tokens of different headache disorder groups

  7. Migraine Disabillity Assessment [MIDAS] score calculation with text input

    Time frame: 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.

  8. Migraine Specific Questionaire versie 2.1 [MSQv2.1] score calculation with text input

    Time frame: 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.

  9. RAND SF-36 Dutch version score calculation with text input

    Time frame: 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.

Sponsors and collaborators

Lead sponsor

University Hospital, Ghent

Other

Registry information

Official study title

NLP Headache Open: an Open Internet-based Survey and Natural Language Processing Project Analysing Written Monologues by Headache Patients

Acronym: NLPH-OPEN

Important dates

Study start
2021
Primary completion
2023
Study completion
2024
First posted
Dec 10, 2021
Registry last updated
May 16, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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