当前位置: X-MOL 学术Pain Pract. › 论文详情
Subgrouping Factors Influencing Migraine Intensity in Women: A Semi-automatic Methodology Based on Machine Learning and Information Geometry.
Pain Practice ( IF 2.258 ) Pub Date : 2019-12-02 , DOI: 10.1111/papr.12854
Francisco J Pérez-Benito,J Alberto Conejero,Carlos Sáez,Juan M García-Gómez,Esperanza Navarro-Pardo,Lidiane L Florencio,César Fernández-de-Las-Peñas

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.
更新日期:2020-03-27

 

全部期刊列表>>
材料学研究精选
Springer Nature Live 产业与创新线上学术论坛
胸腔和胸部成像专题
自然科研论文编辑服务
ACS ES&T Engineering
ACS ES&T Water
屿渡论文,编辑服务
杨超勇
周一歌
华东师范大学
段炼
清华大学
中科大
唐勇
跟Nature、Science文章学绘图
隐藏1h前已浏览文章
中洪博元
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
x-mol收录
福州大学
南京大学
王杰
左智伟
电子显微学
何凤
洛杉矶分校
吴杰
赵延川
试剂库存
天合科研
down
wechat
bug