Search results for "Medo"

showing 7 items of 17 documents

Estudio del estado nutricional de estudiantes de educación primaria y secundaria de la provincia de Valencia y su relación con la adherencia a la Die…

2014

Introducción: La obesidad infantil está alcanzando cifras muy elevadas entre la población infantil española favoreciendo la obesidad en la edad adulta. El objetivo del presente estudio es conocer el estado nutricional de estudiantes de seis colegios de la provincia de Valencia y su grado de adhesión a la Dieta Mediterránea (DM).Material y métodos: Estudio transversal en el que participaron 777 estudiantes, 49,9% eran chicos y 50,1% chicas con edades comprendidas entre 8 y 16 años. Se determinó el peso y la altura de cada participante y se calculó el Índice de Masa Corporal (IMC). A partir del IMC se calculó el Z-score. Para determinar el grado de adhesión a la DM se utilizó el test KidMed.R…

RC620-627Mediterranean dietObesidadOverweightChildhood obesitymedicineTX341-641Nutritional diseases. Deficiency diseasesEating habitsNutrition and DieteticsNutrition. Foods and food supplybusiness.industryNutritional statusmedicine.diseaseObesityComedor escolarSobrepesoEscuelaServicios de alimentaciónCateringmedicine.symptombusinessBody mass indexFood ScienceDemographyPediatric populationRevista Española de Nutrición Humana y Dietética
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A fast and recursive algorithm for clustering large datasets with k-medians

2012

Clustering with fast algorithms large samples of high dimensional data is an important challenge in computational statistics. Borrowing ideas from MacQueen (1967) who introduced a sequential version of the $k$-means algorithm, a new class of recursive stochastic gradient algorithms designed for the $k$-medians loss criterion is proposed. By their recursive nature, these algorithms are very fast and are well adapted to deal with large samples of data that are allowed to arrive sequentially. It is proved that the stochastic gradient algorithm converges almost surely to the set of stationary points of the underlying loss criterion. A particular attention is paid to the averaged versions, which…

Statistics and ProbabilityClustering high-dimensional dataFOS: Computer and information sciencesMathematical optimizationhigh dimensional dataMachine Learning (stat.ML)02 engineering and technologyStochastic approximation01 natural sciencesStatistics - Computation010104 statistics & probabilityk-medoidsStatistics - Machine Learning[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]stochastic approximation0202 electrical engineering electronic engineering information engineeringComputational statisticsrecursive estimatorsAlmost surely[ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST]0101 mathematicsCluster analysisComputation (stat.CO)Mathematicsaveragingk-medoidsRobbins MonroApplied MathematicsEstimator[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]stochastic gradient[ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH]MedoidComputational MathematicsComputational Theory and Mathematicsonline clustering020201 artificial intelligence & image processingpartitioning around medoidsAlgorithm
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Tiešsaistes Klientu Segmentācija, Izmantojot Klasterizācijas Metodes

2021

Bakalaura darba mērķis ir izpētīt tiešsaistes klientu segmentāciju, lai tā palīdzētu pieņemt loģiskus lēmumus par efektīvu mārketinga un reklāmas resursu izmantošanu. Darbā tika izmantotas divas klasterizācijas metodes: K-Medoīdu (K-Medoids) klasterizācija, un K-Prototipu (K-Prototypes) klasterizācija. Metožu izvēle tiek pamatota ar pētītā uzdevuma raksturojumu. Darba gaitā tiek aprakstīti gan abu metožu teorētiskie aspekti, gan metodes tiek pielietotas praktiski (izmantojot programmu R) konkrēta uzdevuma risināšanai. Tika veikta iegūto rezultātu analīze un salīdzināšana. Bakalaura darbā tika arī paskaidrota klientu segmentācijas nozīme veiksmīgam uzņēmumam, kā arī tika aprakstīts interneta…

Tiešsaistes klientu segmentācijaK-Prototipu (K-Prototypes) klasterizācijaVidējā silueta metode (Average silhouette method)MatemātikaK-Medoīdu (K-Medoids) klasterizācijaKlasteru analīze
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Naevus Comedonicus Immunohistochemical Features in Two Cases

2003

Sir, Naevus comedonicus was first described by Kofmann in 1895 (1). It was arranged in groups or linear patterns along Blaschko’s line and located on the face, neck, upper arms, chest and abdomen. It has been well documented that naevus comedonicus occurs as a single entity but occasionally in association with other disorders. The objective of this study was to analyse the clinical and immunohistochemical features of two patients with extensive naevus comedonicus arranged in groups and linear patterns without non-cutaneous abnormalities.

Upper Armsgenetic structuresSingle entitybusiness.industryMedicineImmunohistochemistryDermatologyGeneral MedicineAnatomyskin and connective tissue diseasesbusinesseye diseasesNaevus comedonicusActa Dermato-Venereologica
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Efectividad de una intervención para reducir el miedo a caer en las personas mayores

2014

Objetivo: las caídas y el miedo a caerse se relacionan entre sí, siendo cada uno de ellos factor de riesgo del otro. Este trabajo pretende analizar la efectividad de una intervención para reducir el miedo a caer y sus consecuencias dado que la aplicación de este tipo de tratamientos ha mostrado resultados satisfactorios. Método: mediante un diseño cuasi-experimental, con medidas pre-pos, se evaluó a 53 sujetos, con edades comprendidas entre 65 y 89 años y que habían sufrido una caída anterior. La muestra fue dividida en grupo control y tratamiento, poniéndose en marcha un método combinado de ejercicios y educación sanitaria para la prevención de caídas. Resultados: se obtuvieron resultados …

envelhecimentoagingFearterapêuticaenfermeríaterapéuticaquality of lifenursingqualidade de vidacalidad de vidaMiedotherapeuticsMedoenfermagemenvejecimiento
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Comparative effectiveness of an angiotensin receptor blocker, olmesartan medoxomil, in older hypertensive patients

2018

The efficacy and safety of olmesartan medoxomil (OM) vs active control (AC) monotherapy among elderly patients aged 60‐79 years (N = 4487) was evaluated by meta‐analysis (25 studies). In all patients, change from baseline to end point in blood pressure (BP) was significantly greater with OM vs AC (−19.5/−11.9 vs −16.8/−10.7 mm Hg). Greater proportions of OM‐ vs AC‐treated patients achieved BP goals. In patients with impaired renal function (estimated glomerular filtration rate <60 mL/min/1.73 m(2)), OM treatment resulted in a greater mean change from baseline in systolic BP vs AC (−21.2 vs −18.7 mm Hg, respectively) and a greater proportion of patients achieving BP goals. These parameters w…

medicine.medical_specialtyAngiotensin receptorEndocrinology Diabetes and MetabolismUrologyRenal functionBlood Pressure030204 cardiovascular system & hematologyHypertension Therapy03 medical and health sciencesImpaired renal functionAngiotensin Receptor Antagonists0302 clinical medicineDiabetes mellitusInternal MedicinemedicineHumansIn patient030212 general & internal medicineAdverse effectAgedOlmesartan Medoxomilbusiness.industryMiddle Agedmedicine.diseaseBlood pressureTreatment OutcomeHypertensionCardiology and Cardiovascular MedicineOlmesartanbusinessmedicine.drug
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SparseHC: A Memory-efficient Online Hierarchical Clustering Algorithm

2014

Computing a hierarchical clustering of objects from a pairwise distance matrix is an important algorithmic kernel in computational science. Since the storage of this matrix requires quadratic space with respect to the number of objects, the design of memory-efficient approaches is of high importance to this research area. In this paper, we address this problem by presenting a memory-efficient online hierarchical clustering algorithm called SparseHC. SparseHC scans a sorted and possibly sparse distance matrix chunk-by-chunk. Meanwhile, a dendrogram is built by merging cluster pairs as and when the distance between them is determined to be the smallest among all remaining cluster pairs. The k…

sparse matrixClustering high-dimensional dataTheoretical computer scienceonline algorithmsComputer scienceSingle-linkage clusteringComplete-linkage clusteringNearest-neighbor chain algorithmConsensus clusteringmemory-efficient clusteringCluster analysisk-medians clusteringGeneral Environmental ScienceSparse matrix:Engineering::Computer science and engineering [DRNTU]k-medoidsDendrogramConstrained clusteringHierarchical clusteringDistance matrixCanopy clustering algorithmGeneral Earth and Planetary SciencesFLAME clusteringHierarchical clustering of networkshierarchical clusteringAlgorithmProcedia Computer Science
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