0000000000531567

AUTHOR

Lidiane Lima Florencio

0000-0003-3290-3661

showing 3 related works from this author

Patient Profiling Based on Spectral Clustering for an Enhanced Classification of Patients with Tension-Type Headache

2020

Profiling groups of patients in clusters can provide meaningful insights into the features of the population, thus helping to identify people at risk of chronification and the development of specific therapeutic strategies. Our aim was to determine if spectral clustering is able to distinguish subgroups (clusters) of tension-type headache (TTH) patients, identify the profile of each group, and argue about potential different therapeutic interventions. A total of 208 patients (n = 208) with TTH participated. Headache intensity, frequency, and duration were collected with a 4-week diary. Anxiety and depressive levels, headache-related burden, sleep quality, health-related quality of life, pre…

medicine.medical_specialtyPressure painPopulationgroupslcsh:Technologysensitizationlcsh:Chemistry03 medical and health sciences0302 clinical medicineQuality of lifePatient profilingInternal medicinemedicineGeneral Materials SciencepaineducationInstrumentationlcsh:QH301-705.5030304 developmental biologyFluid Flow and Transfer Processes0303 health scienceseducation.field_of_studyspectral clusteringSleep qualitybusiness.industrylcsh:TProcess Chemistry and TechnologyGeneral Engineeringtension-type headacheSpectral clusteringlcsh:QC1-999Computer Science ApplicationsPsicologialcsh:Biology (General)lcsh:QD1-999lcsh:TA1-2040Anxietymedicine.symptombusinesslcsh:Engineering (General). Civil engineering (General)030217 neurology & neurosurgerylcsh:PhysicsApplied Sciences
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Subgrouping factors influencing migraine intensity in women: A semi-automatic methodology based on machine learning and information geometry

2019

[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 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 (S…

AdultMigraine DisordersMachine learningcomputer.software_genreMachine LearningDisability Evaluation03 medical and health sciences0302 clinical medicine030202 anesthesiologyMachine learningHumansMedicine03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edadesInformation geometryPhysical ExaminationMigraineMultisource variabilityThesaurus (information retrieval)business.industryMiddle Agedmedicine.diseaseAnesthesiology and Pain MedicineMigraineFISICA APLICADAFemaleArtificial intelligenceSemi automaticbusinessMATEMATICA APLICADAcomputer030217 neurology & neurosurgeryRandom forest
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Electromyographic Activity Evolution of Local Twitch Responses During Dry Needling of Latent Trigger Points in the Gastrocnemius Muscle: A Cross-Sect…

2019

Abstract Objective Trigger points (TrPs) are hypersensitive spots within taut bands of skeletal muscles that elicit referred pain and motor changes. Among the variety of techniques used for treating TrPs, dry needling is one of the most commonly applied interventions. The question of eliciting local twitch responses (LTRs) during TrP dry needling is unclear. Our main aim was to investigate the evolution of the electromyographic (EMG) peak activity of each LTR elicited during dry needling into latent TrPs of the gastrocnemius medialis muscle. Methods Twenty asymptomatic subjects with latent TrPs in the gastrocnemius medialis muscle participated in this cross-sectional study. Changes in EMG s…

medicine.medical_specialtyElectromyographyAsymptomatic03 medical and health sciencesGastrocnemius muscle0302 clinical medicineInternal medicinemedicineHumansMuscle SkeletalMyofascial Pain Syndromes030222 orthopedicsDry needlingReferred painmedicine.diagnostic_testbusiness.industryTrigger PointsGeneral MedicineConfidence intervalData at RestCross-Sectional StudiesAnesthesiology and Pain MedicineDry NeedlingCardiologyNeurology (clinical)Analysis of variancemedicine.symptombusiness030217 neurology & neurosurgeryPain Medicine
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