Impacto da reeducação funcional respiratória na pessoa com derrame pleural : Uma revisão sistemática da literatura

Introdução: O derrame pleural define-se como a acumulação anormal de líquido no espaço pleural, podendo ser causado por um significativo número de situações patológicas e originar importantes complicações, dependendo o seu tratamento da causa e da dimensão do derrame. A reeducação funcional respiratória, levada a efeito pelos enfermeiros especialistas em enfermagem de reabilitação, ao englobar um conjunto de técnicas que atuam na respiração com implicações diretas na mecânica alveolar, é vista como uma intervenção potenciadora da redução dos sintomas e otimização da funcionalidade. Neste contexto, o objetivo deste estudo pretende determinar de que forma a reeducação funcional respiratória tem impacto nas pessoas com derrame pleural. Métodos: Foi realizada uma revisão sistemática da literatura sobre estudos que avaliavam o impacto da reeducação funcional respiratória no derrame pleural. Fez-se pesquisa na PUBMED, EBSCO, Google Académico e SciELO de estudos publicados entre janeiro de 2008 e maio de 2017 que foram posteriormente avaliados, respeitando os critérios de inclusão e exclusão previamente estabelecidos. Resultados: Três estudos preencheram os critérios de inclusão, cujos resultados revelam que em pessoas com derrame pleural pretende-se, com a reeducação funcional respiratória, impedir a formação de aderências pleurais, evitar a limitação da mobilidade toraco-pulmonar e diafragmática; impedir ou corrigir as posições antiálgicas defeituosas e as suas consequências, impedir as deformações posturais como a retração do hemitórax comprometido e limitação da articulação escápulo-umeral; incentivar a expansão pulmonar e promover a reabsorção do derrame pleural com a finalidade de melhorar a performance pulmonar. Também as evidências encontradas nos permitiram elaborar um plano de intervenção direccionado à pessoa com derrame pelural. Conclusão: O programa de reeducação funcional respiratória é uma mais-valia como tratamento coadjuvante, trazendo benefícios significativos para as pessoas ao nível da performance pulmonar, assim como na diminuição do tempo de internamento. Palavras-chave: Reabilitação Respiratória, Exercícios Respiratórios; Reeducação Funcional Respiratória, Derrame Pleural.Abstract Introduction: Pleural effusion is defined as the abnormal accumulation of fluid in the pleural space, which can be caused by a significant number of pathological conditions and cause major complications, depending on the treatment of the cause and size of the effusion. Respiratory functional reeducation, carried out by nurses specialized in rehabilitation nursing, encompassing a set of breathing techniques with direct implications in alveolar mechanics, is seen as an intervention that enhances the reduction of symptoms and optimization of functionality. In this context, the aim of this study is to determine how functional respiratory reeducation affects people with pleural effusion. Methods: A systematic review of the literature on studies evaluating the impact of respiratory functional reeducation on pleural effusion was carried out. PUBMED, EBSCO, Google Scholar and SciELO were searched for studies published between January 2008 and May 2017 that were subsequently evaluated, respecting the previously established inclusion and exclusion criteria. RESULTS: Three studies fulfilled the inclusion criteria, and the results show that in people with pleural effusion it is intended, with functional respiratory reeducation, to prevent the formation of pleural adhesions, to avoid the limitation of thoroco-pulmonary and diaphragmatic mobility; preventing or correcting defective analgesic positions and their consequences, preventing postural deformations such as compromised hemithorax retraction and limitation of the sputum-humeral joint; encourage lung expansion and promote the reabsorption of pleural effusion in order to improve pulmonary performance. Also the evidences found allowed us to elaborate a plan of intervention directed to the person with pelural effusion. Conclusion: The respiratory functional re-education program is an added value as an adjunct treatment, bringing significant benefits to patients in terms of pulmonary performance and decreased length of hospital stay. Keywords: Respiratory Rehabilitation, Respiratory Exercises; Respiratory Functional Reeducation, Pleural Effusion

Combining information from distributed evolutionary k-means, in:

Abstract-One of the challenges for clustering resides in dealing with huge amounts of data, which causes the need for distribution of large data sets in separate repositories. However, most clustering techniques require the data to be centralized. One of them, the k-means, has been elected one of the most influential data mining algorithms. Although exact distributed versions of the k-means algorithm have been proposed, the algorithm is still sensitive to the selection of the initial cluster prototypes and requires that the number of clusters be specified in advance. This work tackles the problem of generating an approximated model for distributed clustering, based on k-means, for scenarios where the number of clusters of the distributed data is unknown. We propose a collection of algorithms that generate and select k-means clustering for each distributed subset of the data and combine them afterwards. The variants of the algorithm are compared from two perspectives: the theoretical one, through asymptotic complexity analyses; and the experimental one, through a comparative evaluation of results obtained from a collection of experiments and statistical tests

A cluster based hybrid feature selection approach

Data collection and storage capacities have increased significantly in the past decades. In order to cope with the increasingly complexity of data, feature selection methods have become an omnipresent preprocessing step in data analysis. In this paper we present a hybrid (filter — wrapper) feature selection method tailored for data classification problems. Our hybrid approach is composed of two stages. In the first stage, a filter clusters features to identify and remove redundancy. In the second stage, a wrapper evaluates different feature subsets produced by the filter, determining the one that produces the best classification performance in terms of accuracy. The effectiveness of our method is demonstrated through an empirical evaluation performed on real-world datasets coming from various sources.FAPESP (Grant #2011/04247-5 and #2013/18698-4)CNPq (Grant #304137/2013-8

The Area Under the ROC Curve as a Measure of Clustering Quality

The Area Under the the Receiver Operating Characteristics (ROC) Curve, referred to as AUC, is a well-known performance measure in the supervised learning domain. Due to its compelling features, it has been employed in a number of studies to evaluate and compare the performance of different classifiers. In this work, we explore AUC as a performance measure in the unsupervised learning domain, more specifically, in the context of cluster analysis. In particular, we elaborate on the use of AUC as an internal/relative measure of clustering quality, which we refer to as Area Under the Curve for Clustering (AUCC). We show that the AUCC of a given candidate clustering solution has an expected value under a null model of random clustering solutions, regardless of the size of the dataset and, more importantly, regardless of the number or the (im)balance of clusters under evaluation. In addition, we elaborate on the fact that, in the context of internal/relative clustering validation as we consider, AUCC is actually a linear transformation of the Gamma criterion from Baker and Hubert (1975), for which we also formally derive a theoretical expected value for chance clusterings. We also discuss the computational complexity of these criteria and show that, while an ordinary implementation of Gamma can be computationally prohibitive and impractical for most real applications of cluster analysis, its equivalence with AUCC actually unveils a much more efficient algorithmic procedure. Our theoretical findings are supported by experimental results. These results show that, in addition to an effective and robust quantitative evaluation provided by AUCC, visual inspection of the ROC curves themselves can be useful to further assess a candidate clustering solution from a broader, qualitative perspective as well.Comment: 37 pages, 5 figures, submitted for publicatio

Model selection for semi-supervised clustering

Although there is a large and growing literature that tackles the semi-supervised clustering problem (i.e., using some labeled objects or cluster-guiding constraints like \must-link" or \cannot-link"), the evaluation of semi-supervised clustering approaches has rarely been discussed. The application of cross-validation techniques, for example, is far from straightforward in the semi-supervised setting, yet the problems associated with evaluation have yet to be addressed. Here we\ud summarize these problems and provide a solution.\ud Furthermore, in order to demonstrate practical applicability of semi-supervised clustering methods, we provide a method for model selection in semi-supervised clustering based on this sound evaluation procedure. Our method allows the user to select, based on the available information\ud (labels or constraints), the most appropriate clustering model (e.g., number of clusters, density-parameters) for a given problem.NSERC (Canada)FAPESP (Brazil)CNPq (Brazil

Combining information from distributed evolutionary k-means

One of the challenges for clustering resides in dealing with huge amounts of data, which causes the need for distribution of large data sets in separate repositories. However, most clustering techniques require the data to be centralized. One of them, the k-means, has been elected one of the most influential data mining algorithms. Although exact distributed versions of the k-means algorithm have been proposed, the algorithm is still sensitive to the selection of the initial cluster prototypes and requires that the number of clusters be specified in advance. This work tackles the problem of generating an approximated model for distributed clustering, based on k-means, for scenarios where the number of clusters of the distributed data is unknown. We propose a collection of algorithms that generate and select k-means clustering for each distributed subset of the data and combine them afterwards. The variants of the algorithm are compared from two perspectives: the theoretical one, through asymptotic complexity analyses, and the experimental one, through a comparative evaluation of results obtained from a collection of experiments and statistical tests

Data perturbation for outlier detection ensembles

Outlier detection and ensemble learning are well established research directions in data mining yet the application of ensemble techniques to outlier detection has been rarely studied. Building an ensemble requires learning of diverse models and combining these diverse models in an appropriate way. We propose data perturbation as a new technique to induce diversity in individual outlier detectors as well as a rank accumulation method for the combination of the individual outlier rankings in order to construct an outlier detection ensemble. In an extensive evaluation, we study the impact, potential, and shortcomings of this new approach for outlier detection ensembles. We show that this ensemble can significantly improve over weak performing base methods

Density-based clustering validation

One of the most challenging aspects of clustering is validation, which is the objective and quantitative assessment of clustering results. A number of different relative validity criteria have been proposed for the validation of globular, clusters. Not all data, however, are composed of globular clusters. Density-based clustering algorithms seek partitions with high density areas of points (clusters, not necessarily globular) separated by low density areas, possibly containing noise objects. In these cases relative validity indices proposed for globular cluster validation may fail. In this paper we propose a relative validation index for density-based, arbitrarily shaped clusters. The index assesses clustering quality based on the relative density connection between pairs of objects. Our index is formulated on the basis of a new kernel density function, which is used to compute the density of objects and to evaluate the within- and between-cluster density connectedness of clustering results. Experiments on synthetic and real world data show the effectiveness of our approach for the evaluation and selection of clustering algorithms and their respective appropriate parameters.NSERC (Canada)FAPESP (grants #2012=15751-9, #2010=20032-6 and #2013=18698-4)CNPq (grant #301241 2010-4

Exploiting different users' interactions for profiles enrichment in recommender systems

User profiling is an important aspect of recommender systems. It models users' preferences and is used to assess an item's relevance to a particular user. In this paper we propose a profiling approach which describes and enriches the users' preferences using multiple types of interactions. We show in our experiments that the enriched version of users' profiles is able to provide better recommendations.São Paulo Research Foundation (FAPESP) (grants 2013/18698-4 and 2013/22547-1)CNPq (grant 304137/2013-8