To what extent are patients with migraine able to predict attacks?

Gago-Veiga, Ana B; Pagán, Josué; Henares, Kevin; Heredia, Patricia; González-García, Nuria; De Orbe, María-Irene; Ayala, Jose L; Sobrado, Mónica; Vivancos, Jose

Gago-Veiga, Ana B; Pagán, Josué; Henares, Kevin; Heredia, Patricia; González-García, Nuria; De Orbe, María-Irene; Ayala, Jose L; Sobrado, Mónica; Vivancos, Jose.

Afiliação

Gago-Veiga AB; Headache Unit, Department of Neurology, Instituto de Investigación Sanitaria Hospital, Universitario de la Princesa, Madrid, Spain, dra.anagago@gmail.com.
Pagán J; Department of Electronic Engineering, Universidad Politécnica de Madrid, Madrid, Spain.
Henares K; Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain.
Heredia P; Department of Electronic Engineering, Universidad Politécnica de Madrid, Madrid, Spain.
González-García N; Department of Computer and Automation Architecture, Universidad Complutense de Madrid, Madrid, Spain.
De Orbe MI; Headache Unit, Department of Neurology, Instituto de Investigación Sanitaria Hospital, Universitario de la Princesa, Madrid, Spain, dra.anagago@gmail.com.
Ayala JL; Headache Unit, Hospital Clínico Universitario San Carlos, Madrid, Spain.
Sobrado M; Department of Computer and Automation Architecture, Universidad Complutense de Madrid, Madrid, Spain.
Vivancos J; Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain.

J Pain Res ; 11: 2083-2094, 2018.

Article em En | MEDLINE | ID: mdl-30310310

Palavras-chave

electronic diary; machine learning; migraine; prediction; premonitory symptoms; real-time

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2018 Tipo de documento: Article

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