Perception of Portuguese nurses: clinical supervision and quality indicators in nursing care

Objectives: to describe nurses’ perception of the influence of clinical supervision on improving quality indicators in nursing care. Methods: exploratory research with a qualitative approach, carried out with 16 nurses using the focus group. Data processing emerged from lexicographical textual analysis, resorting to Descending Hierarchical Classification and similarity analysis. Results: 80.0% retention of 185 text segments with six-class construction. The words were represented by four graphs (supervisor, audit, care, and process); and three subgraphs (implementation, sharing and knowledge). Final Considerations: in the perception of nurses, supervision influences quality indicators in nursing care.info:eu-repo/semantics/publishedVersio

BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis

In the biomedical domain, taxonomies organize the acquisition modalities of scientific images in hierarchical structures. Such taxonomies leverage large sets of correct image labels and provide essential information about the importance of a scientific publication, which could then be used in biocuration tasks. However, the hierarchical nature of the labels, the overhead of processing images, the absence or incompleteness of labeled data, and the expertise required to label this type of data impede the creation of useful datasets for biocuration. From a multi-year collaboration with biocurators and text-mining researchers, we derive an iterative visual analytics and active learning strategy to address these challenges. We implement this strategy in a system called BI-LAVA Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis. BI-LAVA leverages a small set of image labels, a hierarchical set of image classifiers, and active learning to help model builders deal with incomplete ground-truth labels, target a hierarchical taxonomy of image modalities, and classify a large pool of unlabeled images. BI-LAVA's front end uses custom encodings to represent data distributions, taxonomies, image projections, and neighborhoods of image thumbnails, which help model builders explore an unfamiliar image dataset and taxonomy and correct and generate labels. An evaluation with machine learning practitioners shows that our mixed human-machine approach successfully supports domain experts in understanding the characteristics of classes within the taxonomy, as well as validating and improving data quality in labeled and unlabeled collections.Comment: 15 pages, 6 figure

Computer-aided detection of simultaneous abdominal organ from CT images based on iterative watershed transform

The interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis applications. Segmentation of the liver, spleen, and kidneys is regarded as a major primary step in computer-aided diagnosis of abdominal organ diseases. In this paper, a semi-automated method for medical image data is presented for abdominal organ segmentation data using mathematical morphology. Our proposed method is based on a hierarchical segmentation and watershed algorithm. In our approach, a powerful technique has been designed to suppress over-segmentation based on a mosaic image and on the computation of the watershed transform. Our algorithm is currently in two parts. In the first, we seek to improve the quality of the gradient-mosaic image. In this step, we propose a method for improving the gradient-mosaic image by applying the anisotropic diffusion filter followed by the morphological filters. Thereafter, we proceed to the hierarchical segmentation of the liver, spleen, and kidney. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work

Multiresolution volume visualitzacion with a texture-based octree

Although 3D texture-based volume rendering guarantees image quality almost interactively, it is difficult to maintain an interactive rate when the technique has to be exploited on large datasets. In this paper, we propose a new texture memory representation and a management policy that substitute the classical one-texel per voxel approach for a hierarchical approach. The hierarchical approach benefits nearly homogeneous regions and regions of lower interest. The proposed algorithm is based on a simple traversal of the octree representation of the volume data. Driven by a user-defined image quality, defined as a combination of data homogeneity and importance, a set of octree nodes (the cut) is selected to be rendered. The degree of accuracy applied for the representation of each one of the nodes of the cut in the texture memory is set independently according to the user-defined parameters. The variable resolution texture model obtained reduces the texture memory size and thus texture swapping, improving rendering speed.Postprint (published version

Hierarchical multi-label classification for protein function prediction going beyond traditional approaches

Hierarchical multi-label classification is a variant of traditional classification in which the instances can belong to several labels, that are in turn organized in a hierarchy. Functional classification of genes is a challenging problem in functional genomics due to several reasons. First, each gene participates in multiple biological activities. Hence, prediction models should support multi-label classification. Second, the genes are organized and classified according to a hierarchical classification scheme that represents the relationships between the functions of the genes. These relationships should be maintained by the prediction models. In addition, various bimolecular data sources, such as gene expression data and protein-protein interaction data, can be used to assign biological functions to genes. Therefore, the integration of multiple data sources is required to acquire a precise picture of the roles of the genes in the living organisms through uncovering novel biology in the form of previously unknown functional annotations. In order to address these issues, the presented work deals with the hierarchical multi-label classification. The purpose of this thesis is threefold: first, Hierarchical Multi-Label classification algorithm using Boosting classifiers, HML-Boosting, for the hierarchical multi-label classification problem in the context of gene function prediction is proposed. HML-Boosting exploits the predefined hierarchical dependencies among the classes. We demonstrate, through HML-Boosting and using two approaches for class-membership inconsistency correction during the testing phase, the top-down approach and the bottom-up approach, that the HMLBoosting algorithm outperforms the flat classifier approach. Moreover, the author proposed the HiBLADE algorithm (Hierarchical multi-label Boosting with LAbel DEpendency), a novel algorithm that takes advantage of not only the pre-established hierarchical taxonomy of the classes, but also effectively exploits the hidden correlation among the classes that is not shown through the class hierarchy, thereby improving the quality of the predictions. According to the proposed approach, first, the pre-defined hierarchical taxonomy of the labels is used to decide upon the training set for each classifier. Second, the dependencies of the children for each label in the hierarchy are captured and analyzed using Bayes method and instance-based similarity. The primary objective of the proposed algorithm is to find and share a number of base models across the correlated labels. HiBLADE is different than the conventional algorithms in two ways. First, it allows the prediction of multiple functions for genes at the same time while maintaining the hierarchy constraint. Second, the classifiers are built based on the label understudy and its most similar sibling. Experimental results on several real-world biomolecular datasets show that the proposed method can improve the performance of hierarchical multilabel classification. More important, however, is then the third part that focuses on the integration of multiple heterogeneous data sources for improving hierarchical multi-label classification. Unlike most of the previous works, which mostly consider a single data source for gene function prediction, the author explores the integration of heterogeneous data sources for genome-wide gene function prediction. The integration of multiple heterogeneous data sources is addressed with a novel Hierarchical Bayesian iNtegration algorithm, HiBiN, a general framework that uses Bayesian reasoning to integrate heterogeneous data sources for accurate gene function prediction. The system formally uses posterior probabilities to assign class memberships to samples using multiple data sources while maintaining the hierarchical constraint that governs the annotation of the genes. The author demonstrates, through HiBiN, that the integration of the diverse datasets significantly improves the classification quality for hierarchical gene function prediction in terms of several measures, compared to single-source prediction models and fused-flat model, which are the baselines compared against. Moreover, the system has been extended to include a weighting scheme to control the contributions from each data source according to its relevance to the label under-study. The results show that the new weighting scheme compares favorably with the other approach along various performance criteria