Unveiling new insights into migraine risk stratification using machine learning models of adjustable risk factors
Integrating causal inference, predictive modeling, and burden projection, this study establishes hierarchical evidence for adjustable migraine determinants and translates findings into scalable prevention frameworks. These findings bridge the gap between biological mechanisms, clinical practice, and public health policy, providing a tripartite framework that harmonizes causal inference, individualized risk prediction, and global burden mapping for migraine prevention.
Authors
- Wei Wang (ORCID: https://orcid.org/0000-0003-2302-6273)
- Yehai Liu (ORCID: https://orcid.org/0000-0003-3977-2481)
- Hai‐Feng Pan (ORCID: https://orcid.org/0000-0001-8218-5747)
- Yu‐Chen Liu (ORCID: https://orcid.org/0000-0002-9189-3021)
Institutions
- Anhui Medical University (CN)
- Sir Run Run Shaw Hospital (CN)
- First Affiliated Hospital of Anhui Medical University (CN)
Publication Details
- Journal
- The Journal of Headache and Pain
- Published
- 2025-05-06
- DOI
- https://doi.org/10.1186/s10194-025-02049-5
- Citations
- 10
- Primary Topic
- Migraine and Headache Studies
- Type
- article
- Field-Weighted Citation Impact
- 14.71
Funders
- Sir Run Run Shaw Hospital
- National Natural Science Foundation of China
- Anhui Medical University
- Zhejiang University