Real-world evidence on migraine treatment in routine clinical practice: insights from the Türkiye migraine registry study using artificial intelligence algorithms
Frontiers in Neurology, vol.17, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 17
- Publication Date: 2026
- Doi Number: 10.3389/fneur.2026.1848551
- Journal Name: Frontiers in Neurology
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE, Psycinfo, Directory of Open Access Journals
- Keywords: artificial intelligence, electronic health records, migraine, natural language processing, real-world evidence, Türkiye
- Open Archive Collection: AVESIS Open Access Collection
- Acibadem Mehmet Ali Aydinlar University Affiliated: Yes
Abstract
Background – Despite available diagnostic and therapeutic options, a major unmet need persists in migraine care. This study applied artificial intelligence (AI) to extract structured insights from routine care and assess migraine burden, diagnosis, and treatment in Türkiye. Methods – The Türkiye Migraine Registry Study was a retrospective, observational study across 23 hospitals and clinics in eight cities. Anonymized electronic health records (EHRs) of 11, 023 adults treated between January 2021 and October 2023 were analyzed. A validated AI framework with natural language processing and machine learning extracted demographics, comorbidities, imaging, and prescriptions. Descriptive statistics summarized outcomes. Results – Of 90, 381 neurology patients, 16, 751 (18.5%) had migraine or suspected migraine; 11, 023 met inclusion. Only 46.9% carried an ICD-10 migraine code, while 53.1% were coded under other headache syndromes yet frequently received migraine-specific therapies. The cohort was 73% female, mean age 41.5. Neuroimaging was frequent (58.7% MRI, 15.2% CT), and 18.4% visited emergency care. Preventive therapy was prescribed to 63.7%, mainly antidepressants (31.4%) and beta-blockers (19.4%). OnabotulinumtoxinA and CGRP monoclonal antibodies were used in 10.3 and 3.4%. Conclusion – AI-assisted EHR analysis revealed underdiagnosis, high imaging use, and limited adoption of mechanism-specific preventives in Türkiye, underscoring the need for improved diagnostic accuracy and broader access to modern therapies.