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-