MigraineNCT05377437
Natural Language Processing for Headache Medicine
A study testing Natural Language Processing in people with Migraine and Headache Disorders, run by University Hospital, Ghent at 2 sites in Belgium.
Trial summary
Headache disorders are diagnosed by clinical history taking and applying the criteria provided within the International Classification of Headache Disorders Third Edition (ICHD-3). Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Migraine, Headache Disorders
- What is being tested?
- Natural Language Processing
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 187 participants
- Where
- 2 sitesBelgium
- Study dates
- 28 Aug 2020to 31 Dec 2023
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Patients visiting the headache clinic of Ghent University Hospital for the first time or in follow up.
- Patients older than 18 years of age.
- Patients should be able to have Dutch as their mother tongue, and be sufficiently able to read, write, understand and speak Dutch.
Who cannot join
- Patients younger than 18 years of age.
- Patients with a language other than Dutch as mother tongue.
- Patients with substance abuse of alcohol or illicit drugs in the present or past.
Trial locations and contacts
2 sites in 1 country.
Belgium2 sites
Ghent University Hospital
Ghent, Belgie, 9000
University Hospital Ghent
Ghent, 9000
About this study
Headache disorders are diagnosed by clinical history taking and applying the criteria provided within the International Classification of Headache Disorders Third Edition (ICHD-3). To help patients and physicians in making the correct diagnosis, digital technologies based on natural language processing (NLP) approaches may help to identify headache disorders within naturally patient-provided speech. The research aims to develop statistical models through machine-learning NLP applications for the accurate and precise classification of headache disorders with headache expert given ICHD-3 diagnosis as the gold standard. Furthermore, the research also aims to develop statistical models through machine-learning NLP applications for the estimation of impact scores derived from validated headache questionnaires by using texts as input. Patients from the tertiary headache clinic will be recruited to provide oral narrative textual descriptions of their headache attack characteristics and burden of disease related to their headache disorders. The goal of the research is to develop accessible, evidence-based digital medical tools as low-effort applications for the correct diagnosis of headache disorders and estimation of burden of disease due to headache disorders.
Read the detailed description
Headache disorders are among the most prevalent and disabling conditions worldwide . The Global Burden of Disease study 2016 found migraine to be the second most leading cause of disability worldwide. In the group of 18- to 49-year-olds, migraine is the leading cause of disability . Still, many patients do not receive adequate diagnosis or proper headache-specific treatments.
Physicians performing headache medicine need to have an accurate and complete headache history to construct a correct diagnosis and therapeutic plan. The diagnosis ideally needs to made by applying the International Classification of Headache Disorders Third Edition (ICHD-3). This process is essential to make the correct diagnosis within a reasonable amount of time. However, history taking in headache patients faces many challenges. It heavily relies on oral or written communication between them and patients. It is an effortful and time-consuming practice mostly for non-experienced physicians. Misinterpretation by patients or physicians within dialogue may occur and lead to misunderstandings, wrong diagnosis and maltreatment. Often, patients find difficulties to express all characteristics during a single visit to the doctor, leaving a wealth of useful information for the physician unused. Finally, measuring the burden of disease in headache disorders is difficult and mostly done through validated but rigid questionnaires. It may neglect the often complex but natural impact headache disorders have on all dimensions of human lives.
With the notable exception of e-diaries, digital tools for the headache physician are currently not available. Digital technology may offer many solutions to the challenges stated above. Globally, digitization is expanding faster than before. In the developed world, almost every person now has access to digital tools such as computers, smartphones or tablets. More than 3,5 billion people around the world were estimated to have access to the Internet in 2015 . Artificial intelligence (AI) and machine learning (ML) are entering our digital world rapidly, with already multiple use-cases being implemented in medicine. Algorithms in the field of imaging analysis, speech analysis and electronic patient database mining have been explored already to determine which beneficial effects can be derived from these techniques.
With increased computational speed, storage capacity and evolving user interfaces, new digital clinical applications have potential for helping the patient and physician along the trajectory of dealing with headache disorders. One such field within digital sciences is natural language processing (NLP). It uses text as input to generate mathematical models that have the potential to accurately classify and estimate numeric accounts on the basis of grammar, lexical content, sentimental value of words and word embeddings in sentences.
The investigators believe that the correct application of NLP in headache medicine can ultimately improve lives of many headache sufferers by giving correct diagnosis timely and facilitating communication about the burden of disease between patient and physician. This research project aims to develop NLP tools which are able to analyse patient-produced text about their headache problems to accurately diagnose headache disorders and to estimate the impact of headache disorders on patient's lives.
- Conditions
- Migraine, Headache Disorders
- Being tested
- Natural Language Processing
- Keywords
- Headache Disorders, Natural Language Processing
- Medical subject headings
- Migraine Disorders, Headache Disorders
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Natural Language ProcessingOther
Natural Language Processing: classification and regression tasks.
| Study group | Role | What they receive |
|---|---|---|
| Participants | Experimental | Participants of the study Receives: Other: Natural Language Processing |
What is being measured
The primary outcome is the main question the study is designed to answer.
F1-scores classification migraine narrative versus cluster headache narrative
Machine learning classification models applying logistic regression, naive bayes classification and support vector machines based on the textual elements of the patients' narratives, to classify the provide narrative as either migraine or cluster headache. Higher F1-scores suggest better classification results.
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 May 2022
- Study start28 Aug 2020
- Primary completion31 Dec 2023
- Study completion31 Dec 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for MigraineStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 187 participants (actual)
Sponsor and investigators
- Lead sponsor
- University Hospital, Ghent
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05377437
- Sponsor's protocol ID
- BC-08263
- Status last verified
- May 2024
- First submitted
- 25 Apr 2022
- Last updated
- 16 May 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Vandenbussche N, Van Hee C, Hoste V, Paemeleire K. Using natural language processing to automatically classify written self-reported narratives by patients with migraine or cluster headache. J Headache Pain. 2022 Sep 30;23(1):129. doi: 10.1186/s10194-022-01490-0.
PubMed 36180844
Study at a glance
From the registry record, last updated by the sponsor on 16 May 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Natural Language Processing for Headache Medicine
- ClinicalTrials.gov ID
- NCT05377437
- Acronym / sponsor ID
- BC-08263
- Conditions
- Migraine, Headache Disorders
- Keywords
- Headache Disorders, Natural Language Processing
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 187 participants (actual)
- Lead sponsor
- University Hospital, GhentUniversity, hospital or other
- Study start
- 28 Aug 2020 (actual)
- Primary completion
- 31 Dec 2023 (actual)
- Study completion
- 31 Dec 2023 (actual)
- First posted
- 17 May 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Headache disorders are diagnosed by clinical history taking and applying the criteria provided within the International Classification of Headache Disorders Third Edition (ICHD-3). To help patients and physicians in making the correct diagnosis, digital technologies based on natural language processing (NLP) approaches may help to identify headache disorders within naturally patient-provided speech. The research aims to develop statistical models through machine-learning NLP applications for the accurate and precise classification of headache disorders with headache expert given ICHD-3 diagnosis as the gold standard. Furthermore, the research also aims to develop statistical models through machine-learning NLP applications for the estimation of impact scores derived from validated headache questionnaires by using texts as input. Patients from the tertiary headache clinic will be recruited to provide oral narrative textual descriptions of their headache attack characteristics and burden of disease related to their headache disorders. The goal of the research is to develop accessible, evidence-based digital medical tools as low-effort applications for the correct diagnosis of headache disorders and estimation of burden of disease due to headache disorders.
Detailed description
Headache disorders are among the most prevalent and disabling conditions worldwide . The Global Burden of Disease study 2016 found migraine to be the second most leading cause of disability worldwide. In the group of 18- to 49-year-olds, migraine is the leading cause of disability . Still, many patients do not receive adequate diagnosis or proper headache-specific treatments.
Physicians performing headache medicine need to have an accurate and complete headache history to construct a correct diagnosis and therapeutic plan. The diagnosis ideally needs to made by applying the International Classification of Headache Disorders Third Edition (ICHD-3). This process is essential to make the correct diagnosis within a reasonable amount of time. However, history taking in headache patients faces many challenges. It heavily relies on oral or written communication between them and patients. It is an effortful and time-consuming practice mostly for non-experienced physicians. Misinterpretation by patients or physicians within dialogue may occur and lead to misunderstandings, wrong diagnosis and maltreatment. Often, patients find difficulties to express all characteristics during a single visit to the doctor, leaving a wealth of useful information for the physician unused. Finally, measuring the burden of disease in headache disorders is difficult and mostly done through validated but rigid questionnaires. It may neglect the often complex but natural impact headache disorders have on all dimensions of human lives.
With the notable exception of e-diaries, digital tools for the headache physician are currently not available. Digital technology may offer many solutions to the challenges stated above. Globally, digitization is expanding faster than before. In the developed world, almost every person now has access to digital tools such as computers, smartphones or tablets. More than 3,5 billion people around the world were estimated to have access to the Internet in 2015 . Artificial intelligence (AI) and machine learning (ML) are entering our digital world rapidly, with already multiple use-cases being implemented in medicine. Algorithms in the field of imaging analysis, speech analysis and electronic patient database mining have been explored already to determine which beneficial effects can be derived from these techniques.
With increased computational speed, storage capacity and evolving user interfaces, new digital clinical applications have potential for helping the patient and physician along the trajectory of dealing with headache disorders. One such field within digital sciences is natural language processing (NLP). It uses text as input to generate mathematical models that have the potential to accurately classify and estimate numeric accounts on the basis of grammar, lexical content, sentimental value of words and word embeddings in sentences.
The investigators believe that the correct application of NLP in headache medicine can ultimately improve lives of many headache sufferers by giving correct diagnosis timely and facilitating communication about the burden of disease between patient and physician. This research project aims to develop NLP tools which are able to analyse patient-produced text about their headache problems to accurately diagnose headache disorders and to estimate the impact of headache disorders on patient's lives.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Participants | Experimental | Other: Natural Language ProcessingParticipants of the study |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Patients visiting the headache clinic of Ghent University Hospital for the first time or in follow up.
- Patients older than 18 years of age.
- Patients should be able to have Dutch as their mother tongue, and be sufficiently able to read, write, understand and speak Dutch.
Key exclusion criteria (3 listed)
- Patients younger than 18 years of age.
- Patients with a language other than Dutch as mother tongue.
- Patients with substance abuse of alcohol or illicit drugs in the present or past.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Natural Language ProcessingNatural Language Processing: classification and regression tasks. | Other | Participants |
Primary outcome
F1-scores classification migraine narrative versus cluster headache narrative
Time frame: Baseline
Machine learning classification models applying logistic regression, naive bayes classification and support vector machines based on the textual elements of the patients' narratives, to classify the provide narrative as either migraine or cluster headache. Higher F1-scores suggest better classification results.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 May 2024.
- Derived from this study: PubMed 36180844Vandenbussche N, Van Hee C, Hoste V, Paemeleire K. Using natural language processing to automatically classify written self-reported narratives by patients with migraine or cluster headache. J Headache Pain. 2022 Sep 30;23(1):129. doi: 10.1186/s10194-022-01490-0.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Natural Language Processing in people with Migraine and Headache Disorders, run by University Hospital, Ghent at 2 sites in Belgium.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 16 May 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Belgium. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
F1-scores classification migraine narrative versus cluster headache narrative (time frame: Baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05377437, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 16 May 2024; AzTrials last refreshed its copy on 28 Oct 2026.