AIMC Topic: Migraine Disorders

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

Pain practice : the official journal of World Institute of Pain
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.

Two-step deep neural network for segmentation of deep white matter hyperintensities in migraineurs.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Patients with migraine show an increased presence of white matter hyperintensities (WMHs), especially deep WMHs. Segmentation of small, deep WMHs is a critical issue in managing migraine care. Here, we aim to develop a novel...

Intravenous Fluid for the Treatment of Emergency Department Patients With Migraine Headache: A Randomized Controlled Trial.

Annals of emergency medicine
STUDY OBJECTIVE: The objective of this pilot study is to assess the feasibility and necessity of performing a large-scale trial to measure the effect of intravenous fluid therapy on migraine headache pain.

Multimodal MRI-based classification of migraine: using deep learning convolutional neural network.

Biomedical engineering online
BACKGROUND: Recently, deep learning technologies have rapidly expanded into medical image analysis, including both disease detection and classification. As far as we know, migraine is a disabling and common neurological disorder, typically characteri...

Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data.

BMC medical informatics and decision making
BACKGROUND: Feature selection methods are commonly used to identify subsets of relevant features to facilitate the construction of models for classification, yet little is known about how feature selection methods perform in diffusion tensor images (...

Understanding migraine using dynamic network biomarkers.

Cephalalgia : an international journal of headache
BACKGROUND: Mathematical modeling approaches are becoming ever more established in clinical neuroscience. They provide insight that is key to understanding complex interactions of network phenomena, in general, and interactions within the migraine-ge...

Diagnosing migraine from genome-wide genotype data: a machine learning analysis.

Brain : a journal of neurology
Migraine has an assumed polygenic basis, but the genetic risk variants identified in genome-wide association studies only explain a proportion of the heritability. We aimed to develop machine learning models, capturing non-additive and interactive ef...

Multidimensional Feature Analysis of Meniere's Disease and Vestibular Migraine: Insights from Machine Learning and Vestibular Testing.

Journal of the Association for Research in Otolaryngology : JARO
OBJECTIVE: Differentiating between Meniere's disease (MD) and vestibular migraine (VM) is challenging due to overlapping symptoms and limited diagnostic tools. Traditional statistical methods often rely on physician judgment and struggle with complex...

An evolving machine-learning-based algorithm to early predict response to anti-CGRP monoclonal antibodies in patients with migraine.

Cephalalgia : an international journal of headache
BACKGROUND: The present study aimed to determine whether machine-learning (ML)-based models can predict 3-, 6, and 12-month responses to the monoclonal antibodies (mAbs) against the calcitonin gene-related peptide (CGRP) or its receptor (anti-CGRPmAb...

[Digitization in the diagnosis and treatment of headache].

MMW Fortschritte der Medizin
In recent years, machine learning, particularly Natural Language Processing, has emerged as a valuable tool for analyzing unstructured health data, such as headache anamneses. Studies demonstrate that algorithms can identify specific patterns and aut...