Trial reportThe journal of headache and pain2022
Using natural language processing to automatically classify written self-reported narratives by patients with migraine or cluster headache.
Trial report in The journal of headache and pain, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05377437 (Natural Language Processing for Headache Medicine), which is not on this map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Natural Language Processing for Headache Medicine
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Artificial intelligence in headache medicine: between automation and the doctor-patient relationship. A systematic review.The journal of headache and pain · 2025Pooled it
- Utilizing Artificial Intelligence for the Diagnosis, Assessment, and Management of Chronic Pain.Journal of biomedical physics & engineering · 2025Review
- LLM enabled classification of patient self-reported symptoms and needs in health systems across the USA.NPJ digital medicine · 2025Article
- Capturing Patients' Lived Experiences with Chronic Pain through Motivational Interviewing and Information Extraction.Proceedings of the ... Workshop on Patient-Oriented Language Processing · 2025Article
- Review
- Application of Artificial Intelligence in the Headache Field.Current pain and headache reports · 2024Review
- Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members' vision - part 1.The journal of headache and pain · 2024Review
- Advancing the integration of biosignal-based automated pain assessment methods into a comprehensive model for addressing cancer pain.BMC palliative care · 2024Article
- Clinical Applications of Machine Learning.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2024Review
- AI-based disease category prediction model using symptoms from low-resource Ethiopian language: Afaan Oromo text.Scientific reports · 2024Article
- Article
- A Large Language Model-Based Generative Natural Language Processing Framework Finetuned on Clinical Notes Accurately Extracts Headache Frequency from Electronic Health Records.medRxiv : the preprint server for health sciences · 2023Article
- Verification of a clinical decision support system for the diagnosis of headache disorders based on patient-computer interactions: a multi-center study.The journal of headache and pain · 2023Article
- Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundHeadache medicine is largely based on detailed history taking by physicians analysing patients' descriptions of headache. Natural language processing (NLP) structures and processes linguistic data into quantifiable units. In this study, we apply these digital techniques on self-reported narratives by patients with headache disorders to research the potential of analysing and automatically classifying human-generated text and information extraction in clinical contexts.
methodsA prospective cross-sectional clinical trial collected self-reported narratives on headache disorders from participants with either migraine or cluster headache. NLP was applied for the analysis of lexical, semantic and thematic properties of the texts. Machine learning (ML) algorithms were applied to classify the descriptions of headache attacks from individual participants into their correct group (migraine versus cluster headache).
resultsOne-hundred and twenty-one patients (81 participants with migraine and 40 participants with cluster headache) provided a self-reported narrative on their headache disorder. Lexical analysis of this text corpus resulted in several specific key words per diagnostic group (cluster headache: Dutch (nl): "oog" | English (en): "eye", nl: "pijn" | en: "pain" and nl: "terug" | en: "back/to come back"; migraine: nl: "hoofdpijn" | en: "headache", nl: "stress" | en: "stress" and nl: "misselijkheid" | en: "nausea"). Thematic and sentiment analysis of text revealed largely negative sentiment in texts by both patients with migraine and cluster headache. Logistic regression and support vector machine algorithms with different feature groups performed best for the classification of attack descriptions (with F1-scores for detecting cluster headache varying between 0.82 and 0.86) compared to naïve Bayes classifiers.
conclusionsDifferences in lexical choices between patients with migraine and cluster headache are detected with NLP and are congruent with domain expert knowledge of the disorders. Our research shows that ML algorithms have potential to classify patients' self-reported narratives of migraine or cluster headache with good performance. NLP shows its capability to discern relevant linguistic aspects in narratives from patients with different headache disorders and demonstrates relevance in clinical information extraction. The potential benefits on the classification performance of larger datasets and neural NLP methods can be investigated in the future.
trial registrationThis study was registered with clinicaltrials.gov with ID NCT05377437.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.