Subgrouping Factors Influencing Migraine Intensity in Women: A Semi‐automatic Methodology Based on Machine Learning and Information Geometry

Abstract

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.

Machine‐learning‐based approach for predicting response to anti‐calcitonin gene‐related peptide (CGRP) receptor or ligand antibody treatment in patients with migraine: A multicenter Spanish study

“…Machine learning (ML) can help improve these predictions by acquiring knowledge from complex data to find hidden features that can improve predictions using a large dataset. ML approaches have already been used to characterize, classify and predict neurological disorders [20][21][22][23][24] and successful clinical applications of these methods are expected in the next few years.…”

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

“…Out of the total of 27 studies that were identified for the topic of pain, 14 used a prospective cohort design, 197 198 199 200 201 202 203 204 205 206 207 208 209 210 11 used an observational design, 200 204 205 207 210 211 212 213 214 215 216 6 used a retrospective cohort design, 211 214 215 217 218 219 4 used a randomized control trial, 201 212 220 221 1 used a cross-sectional design, 222 and 1 used mixed methods. 223 Most studies used questionnaire/survey data, but eight used administrative databases, 206 207 208 210 212 220 221 222 seven used mobile devices/sensors, 200 203 204 205 210 216 220 and four used a data warehouse or registry. 198 203 208 214 Study populations were mostly done with adults in the outpatient setting but four were inpatient 197 201 211 223 and one was done with a pediatric population.…”

“…205 Although many studies were conducted in the United States, others included China, 213 214 215 222 Australia, 207 Canada, 202 the Netherlands, 199 212 Germany, 208 210 211 Norway, 201 Finland, 204 South Korea, 203 Argentina, 219 Portugal, 197 Japan, 209 and Spain. 206 Sample sizes ranged from 10 to 6,316 observations.…”

Bias Analysis on Public X-Ray Image Datasets of Pneumonia and COVID-19 Patients

“…The histograms of the images may be considered as Probability Density Functions (PDFs) and may serve to measure the variability among gray-level distributions using a methodology based on information geometry [18] . This methodology has been successfully applied to characterize EHR (Electronic Health Record) data [19] , [20] , to assess the variability among patients with different headache pain intensity [21] , or to detect pixel distribution differences among images acquired from different mammographs [22] .…”