Abstract
The use of electronic health records to support health activities is currently quite widespread, due to the increasing complexity of health care and the need to optimize human and material resources. The requirement of health care professionals to access clinical information expeditiously is a key factor in the quality of health care services. Therefore, the assessment of the usability of the health of applications is crucial. The objective of this study was to evaluate the usability of SClinico through a questionnaire especially developed, and it took place in an hospital environment. The SClinico is an electronic health record application used on a large scale in public Portuguese healthcare institutions. The results show that SClinico has some usability issues that need to be improved. Among these we highlight the need to take a holistic view of the patient's health record and simultaneously quick access to relevant details for different clinical situations.

Abstract
Some of the most difficult problems to deal with when using Wireless Sensor Networks (WSNs) are related to the unreliable nature of communication channels. In this context, the use of cooperative diversity techniques and the application of network coding concepts may be promising solutions to improve the communication reliability. In this paper, we propose the NetCoDer scheme to address this problem. Its design is based on merging cooperative diversity techniques and network coding concepts. We evaluate the effectiveness of the NetCoDer scheme through both an experimental setup with real WSN nodes and a simulation assessment, comparing NetCoDer performance against state-of-the-art TDMA-based (Time Division Multiple Access) retransmission techniques: BlockACK, Master/Slave and Redundant TDMA. The obtained results highlight that the proposed NetCoDer scheme clearly improves the network performance when compared with other retransmission techniques.

Abstract

Abstract
We present a study in the area of sentiment analysis of clients' commentaries transcribed by assistants of a help-desk service of one Portuguese telecommunications company. We have adopted a lexicon-based approach to determine the polarity of the sentiment of each commentary, based on the so called opinion words. This task was by no means easy, as not many tools are available for the Portuguese language. The initial results with the off-the-shelf solutions were rather poor. This has motivated us to carry out a number of enhancements, including, for instance, enriching the given lexicon with domain specific terms, formulating specific rules for negation and amplifiers. Automatic pruning of some of the lexicon terms has led to a significant improvement in performance. As our final system achieved a very good performance, our work should be of interest to others working on domain specific solutions for languages where ready-made solutions are not available.

Abstract
In the design of computer-aided diagnosis systems for lung cancer diagnosis, an appropriate and accurate segmentation of the pulmonary nodules in computerized tomography (CT) is one of the most relevant and difficult tasks. An accurate segmentation is crucial for the posterior measurement of nodule characteristics and for lung cancer diagnosis. This paper proposes different approaches that use Hessian-based strategies for lung nodule segmentation in chest CT scans. We propose a multiscale segmentation process that uses the central medialness adaptive principle, a Hessian-based strategy that was originally formulated for tubular extraction but it also provides good segmentation results in blob-like structures as is the case of lung nodules. We compared this proposal with a well established Hessian-based strategy that calculates the Shape Index (SI) and Curvedness (CV). We adapted the SI and CV approach for multiscale nodule segmentation. Moreover, we propose the combination of both strategies by combining the results, in order to take benefit of the advantages of both strategies. Different cases with pulmonary nodules from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database were taken and used to analyze and validate the approaches. The chest CT images present a large variability in nodule characteristics and image conditions. Our proposals provide an accurate lung nodule segmentation, similar to radiologists performance. Our Hessian-based approaches were validated with 569 solid and mostly solid nodules demonstrating that these novel strategies have good results when compared with the radiologists segmentations, providing accurate pulmonary nodule volumes for posterior characterization and appropriate diagnosis.

Abstract
Gastroenterology imaging is a diagnostic procedure that incorporates various computer vision challenges for the design of assisted diagnostic systems. The most typical challenge is the design of more adequate visual descriptors that can assist the classification algorithms in getting good diagnostic results. Literature shows that most of the texture descriptors for feature extraction from gastric lesions are based on Gabor filters or local binary patterns (LBP). Although good results are obtained, these techniques have their shortcomings. In this paper, we aim to explore the use of fusion of Gabor filters and LBPs for characterizing gastric lesions. The images are first subjected to Gabor filtering using isotropic Gabor filters, followed by extracting LBPs from the filtered images. We validate the performance of the descriptor on a novel gastroenterology dataset: the Post-MAPS dataset. Our results show that the proposed feature set outperforms the other methods that have been considered in this paper.