Subgrouping Factors Influencing Migraine Intensity in Women: A Semi‐automatic Methodology Based on Machine Learning and Information Geometry.
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 techniques. Objective: The aim of this study was to analyze critical features that...
| Publicado en: | Pain Practice Vol. 20; no. 3; pp. 297 - 310 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Mar2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142138374&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142138374 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15307085 HYN jtl: Pain Practice issn: 15307085 maglogo: Y pubinfo: dt: Mar2020 vid: 20 iid: 3 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 142138374 142138374 145842933 142138374 10.1111/papr.12854 142138374 ppf: 297 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Subgrouping Factors Influencing Migraine Intensity in Women: A Semi‐automatic Methodology Based on Machine Learning and Information Geometry. aug: au: Pérez‐Benito, Francisco J. Conejero, J. Alberto Sáez, Carlos García‐Gómez, Juan M. Navarro‐Pardo, Esperanza Florencio, Lidiane L. Fernández‐de‐las‐Peñas, César affil: Biomedical Data Science Lab (BDSLab), Instituto Universitario de las Tecnologías de la Información y Comunicaciones (ITACA), Univeritat Politècnica de València, Valencia, Spain sug: subj: Machine Learning Algorithms Migraine Diagnosis Pain Measurement Women's Health Human Female Scales Psychological Tests Tibialis Anterior Muscle Pathology Metacarpal Bones Pathology Physical Examination State-Trait Anxiety Inventory Flexion Evaluation Rotation Evaluation Sitting Standing Female ab: 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 techniques. Objective: The aim of this study was to analyze critical features that permit the differentiation of subgroups of patients with migraine according to the intensity and frequency of attacks by using machine learning algorithms. Methods: Sixty‐seven women with migraine participated. Clinical features of migraine, related disability (Migraine Disability Assessment Scale), anxiety/depressive levels (Hospital Anxiety and Depression Scale), anxiety state/trait levels (State‐Trait Anxiety Inventory), and pressure pain thresholds (PPTs) over the temporalis, neck, second metacarpal, and tibialis anterior were collected. Physical examination included the flexion‐rotation test, cervical range of cervical motion, forward head position while sitting and standing, passive accessory intervertebral movements (PAIVMs) with headache reproduction, and joint positioning sense error. Subgrouping was based on machine learning algorithms by using the nearest neighbors algorithm, multisource variability assessment, and random forest model. Results: For migraine intensity, group 2 (women with a regular migraine headache intensity score of 7 on an 11‐point Numeric Pain Rating Scale [where 0 = no pain and 10 = maximum pain]) were younger and had lower joint positioning sense error in cervical rotation, greater cervical mobility in rotation and flexion, lower flexion‐rotation test scores, positive PAIVMs reproducing migraine, normal PPTs over the tibialis anterior, shorter migraine history, and lower cranio‐vertebral angles while standing than the remaining migraine intensity subgroups. The most discriminative variable was the flexion‐rotation test score of the symptomatic side. For migraine frequency, no model was able to identify differences between groups (ie, patients with episodic or chronic migraine). Conclusions: A subgroup of women with migraine who had common migraine intensity was identified with machine learning algorithms. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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