654 research outputs found
Ejemplificación de la teoría de función poética jakobsoniana en la "Canción de Grisóstomo" de Cervantes
Ejemplificación de la teoría de función poética jakobsoniana en la "Canción de Grisóstomo" de Cervantes
Evaluation of good practices about breastfeeding in a mother and child hospital
Fundamentos: La protección, promoción y
apoyo a la lactancia materna (LM) está considerada como un área de atención prioritaria de salud
pública, así como un factor determinante de la salud infantil y materna. La utilización de guías de
buenas prácticas mejora los resultados de salud y
la seguridad de los pacientes. El objetivo del estudio fue evaluar el impacto en la lactancia materna
tras la implantación de la Guía de Buenas Prácticas
Clínicas “Lactancia Materna” en el centro Materno
Infantil del Hospital Universitario Virgen de las
Nieves de Granada.
Métodos: Se realizó un estudio descriptivo transversal. Se estudiaron madres y recién nacidos atendidos desde 2015 a 2018. Se examinaron variables de
proceso y de resultados, realizando análisis descriptivo y bivariante para la comparativa entre años.
Resultados: La tasa de LM exclusiva al alta pasó
del 58,3 % al 72,2 %. Se encontraron diferencias significativas para la primera toma de LM exclusiva en
los partos eutócicos (del 90,8% al 93,2%) y en las cesáreas (del 21,7% al 60%). Se obtuvieron mejoras en
la valoración de la toma, la educación postnatal y el
inicio y duración del contacto piel con piel.
Conclusiones: Las acciones protocolizadas
posnatales que realizan los profesionales de la salud, como la valoración de la toma de LM y la educación postnatal, resultan eficaces para instaurar la
lactancia. El momento de inicio del contacto piel
con piel y su duración son aspectos que deben ser
reforzados para un apoyo efectivo.Background: Protection, promotion and support to the breastfeeding is considered as an area
of priority in public health care and as a determining factor of child and maternal health. The use of
good practice guides improves health outcomes and
patients safety. The aim of study was to assess the
impact on breastfeeding of a Guide of Good Clinic
Practices about breastfeeding in the Mother and
Child Center of the Virgen de las Nieves University
Hospital in Granada.
Methods: Cross-sectional descriptive study.
Mothers and newborns attended from 2015 to 2018
were studied. Process and outcome variables were
considered to perform a descriptive and bivariate
analysis for the comparison between years.
Results: The rate of exclusive breastfeeding at
discharge went from 58.3% to 72.2%. Significant differences were found for the first intake of exclusive breastfeeding in eutocic births and in C-sections,
from 90.8% to 93.2% in the first and from 21.7% to
60% in the second. Improvements were detected in
the assessment of intake, postnatal education and onset and duration of skin-to-skin contact.
Conclusions: Postnatal protocolized actions
carried out by health professionals, such as the assessment of the intake of breastfeeding and postnatal education, were effective for the establishment
of breastfeeding. The first moment of skin-to-skin
contact and its duration are aspects to be reinforced
for effective support
External clustering validity index based on chi-squared statistical test
Clustering is one of the most commonly used techniques in data mining. Its main goal is
to group objects into clusters so that each group contains objects that are more similar to
each other than to objects in other clusters. The evaluation of a clustering solution is a task
carried out through the application of validity indices. These indices measure the quality
of the solution and can be classified as either internal that calculate the quality of the
solution through the data of the clusters, or as external indices that measure the quality
by means of external information such as the class. Generally, indices from the literature
determine their optimal result through graphical representation, whose results could be
imprecisely interpreted. The aim of this paper is to present a new external validity index
based on the chi-squared statistical test named Chi Index, which presents accurate results
that require no further interpretation. Chi Index was analyzed using the clustering results
of 3 clustering methods in 47 public datasets. Results indicate a better hit rate and a lower
percentage of error against 15 external validity indices from the literature.Ministerio de Economía y Competitividad TIN2014-55894-C2-RMinisterio de Economía y Competitividad TIN2017-88209-C2-2-
An approach to validity indices for clustering techniques in Big Data
Clustering analysis is one of the most used
Machine Learning techniques to discover groups among data
objects. Some clustering methods require the number of clus ters into which the data is going to be partitioned. There exist
several cluster validity indices that help us to approximate
the optimal number of clusters of the dataset. However, such
indices are not suitable to deal with Big Data due to its size
limitation and runtime costs. This paper presents two cluster ing validity indices that handle large amount of data in low
computational time. Our indices are based on redefinitions
of traditional indices by simplifying the intra-cluster distance
calculation. Two types of tests have been carried out over 28
synthetic datasets to analyze the performance of the proposed
indices. First, we test the indices with small and medium size
datasets to verify that our indices have a similar effectiveness
to the traditional ones. Subsequently, tests on datasets of up
to 11 million records and 20 features have been executed to
check their efficiency. The results show that both indices can
handle Big Data in a very low computational time with an
effectiveness similar to the traditional indices using Apache
Spark framework.Ministerio de Economía y Competitividad TIN2014-55894-C2-1-
An Approach to Silhouette and Dunn Clustering Indices Applied to Big Data in Spark
K-Means and Bisecting K-Means clustering algorithms need the optimal number into which the dataset may be divided. Spark implementations of these algorithms include a method that is used to calculate this number. Unfortunately, this measurement presents a lack of precision because it only takes into account a sum of intra-cluster distances misleading the results. Moreover, this measurement has not been well-contrasted in previous researches about clustering indices. Therefore, we introduce a new Spark implementation of Silhouette and Dunn indices. These clustering indices have been tested in previous works. The results obtained show the potential of Silhouette and Dunn to deal with Big Data.Ministerio de Economía y Competitividad TIN2014-55894-C2-1-
Aproximación al índice externo de validación de clustering basado en chi cuadrado
El clustering es una de las técnicas más utilizadas en
minería de datos. Tiene como objetivo principal agrupar datos
en clusters de manera que los objetos que pertenecen al mismo
clúster sean más similares que los que pertenecen a diferentes
clusters. La validación de un clustering es una tarea que se realiza
aplicando los llamados índices de validación. Estos índices miden
la calidad de la solución del clustering y se podrían clasificar
como índices internos, los cuales calculan la calidad del clustering
en función de los propios clusters; e índices externos, que miden la
calidad mediante información externa de los datos, como puede
ser la clase. Los índices externos que nos encontramos en la
literatura están sujetos a una interpretación que puede dar lugar
a error, por ello, el objetivo de este artículo es presentar un nuevo
índice de validación externa basado en el test estadístico de chi
cuadrado que mide la calidad del clustering de forma exacta,
sin necesidad de tener que ser interpretado. Se ha realizado una
experimentación usando 6 datasets que podrían ser considerados
big data y los resultados obtenidos son prometedores ya que
mejoran la tasa de aciertos y porcentaje de error respecto a los
índices de la literatura.Ministerio de Economía y Competitividad TIN2014-55894-C2-
A study of the suitability of autoencoders for preprocessing data in breast cancer experimentation
Breast cancer is the most common cause of cancer death in women. Today, post-transcriptional protein
products of the genes involved in breast cancer can be identified by immunohistochemistry. However,
this method has problems arising from the intra-observer and inter-observer variability in the assess ment of pathologic variables, which may result in misleading conclusions. Using an optimal selection
of preprocessing techniques may help to reduce observer variability. Deep learning has emerged as a
powerful technique for any tasks related to machine learning such as classification and regression. The
aim of this work is to use autoencoders (neural networks commonly used to feed deep learning architec tures) to improve the quality of the data for developing immunohistochemistry signatures with prognos tic value in breast cancer. Our testing on data from 222 patients with invasive non-special type breast
carcinoma shows that an automatic binarization of experimental data after autoencoding could outper form other classical preprocessing techniques (such as human-dependent or automatic binarization only)
when applied to the prognosis of breast cancer by immunohistochemical signaturesMinisterio de Economía y Competitividad TIN2014-55894-C2-1-
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