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

Study Overview
=================
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.

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

Conditions
-----------------
Headache Disorders, Tension-Type Headache, Migraine Disorders, Cluster Headache, TACS, Secondary Headache Disorder

Intervention / Treatment
-----------------
* Other: Questionnaires


Participation Criteria
=================
Eligibility Criteria
-----------------
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

Ages Eligible for Study
-----------------
Minimum Age: 18 Years

Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
No

Study Plan
=================
How is the study designed?
-----------------

Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Participants<br>Participants filling in the questionnaires | Other: Questionnaires<br>* Headache attack descriptions, Headache related disability descriptions, Questionnaires, MIDAS, MSQv2.1, SF36<br>|

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 
| 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 | 
| Word counts | Counts of word tokens of different headache disorder groups | through study completion, an average of 1 year | 
| Sentences counts | Counts of sentence tokens of different headache disorder groups | through study completion, an average of 1 year | 
| Paragraph counts | Counts of paragraph tokens of different headache disorder groups | 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 | 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. | 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. | 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. | through study completion, an average of 1 year | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
Migraine, Natural Language Processing, Headache