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Subgrouping factors influencing migraine intensity in women: A semi-automatic methodology based on machine learning and information geometry

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Subgrouping factors influencing migraine intensity in women: A semi-automatic methodology based on machine learning and information geometry

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Perez-Benito, FJ.; Conejero, JA.; Sáez Silvestre, C.; Garcia-Gomez, JM.; Navarro-Pardo, E.; Florencio, LL.; Fernández-De-Las-Peñas, C. (2020). Subgrouping factors influencing migraine intensity in women: A semi-automatic methodology based on machine learning and information geometry. Pain Practice. 20(3):297-309. https://doi.org/10.1111/papr.12854

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/176265

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Title: Subgrouping factors influencing migraine intensity in women: A semi-automatic methodology based on machine learning and information geometry
Author: Perez-Benito, Francisco Javier Conejero, J. Alberto Sáez Silvestre, Carlos Garcia-Gomez, Juan M Navarro-Pardo, Esperanza Florencio, Lidiane L. Fernández-de-las-Peñas, César
UPV Unit: Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada
Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada
Universitat Politècnica de València. Instituto Universitario de Matemática Pura y Aplicada - Institut Universitari de Matemàtica Pura i Aplicada
Issued date:
Abstract:
[EN] Background Migraine is a heterogeneous condition with multiple clinical manifestations. Machine learning algorithms permit the identification of population groups, providing analytical advantages over other modeling ...[+]
Subjects: Migraine , Random forest , Machine learning , Multisource variability
Copyrigths: Reserva de todos los derechos
Source:
Pain Practice. (issn: 1530-7085 )
DOI: 10.1111/papr.12854
Publisher:
Wiley-Blackwell Publishing
Publisher version: https://doi.org/10.1111/papr.12854
Description: This is the peer reviewed version of the following article: Pérez-Benito, F.J., Conejero, J.A., Sáez, C., García-Gómez, J.M., Navarro-Pardo, E., Florencio, L.L. and Fernández-de-las-Peñas, C. (2020), Subgrouping Factors Influencing Migraine Intensity in Women: A Semi-automatic Methodology Based on Machine Learning and Information Geometry. Pain Pract, 20: 297-309, which has been published in final form at https://doi.org/10.1111/papr.12854. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Type: Artículo

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