BackgroundThe area of the hospital automation has been the subject of much research, addressing relevant issues which can be automated, such as: management and control (electronic medical records, scheduling appointments, hospitalization, among others); communication (tracking patients, staff and materials), development of medical, hospital and laboratory equipment; monitoring (patients, staff and materials); and aid to medical diagnosis (according to each speciality).MethodsIn this context, this paper presents a Fuzzy model for helping medical diagnosis of Intensive Care Unit (ICU) patients and their vital signs monitored through a multiparameter heart screen. Intelligent systems techniques were used in the data acquisition and processing (sorting, transforming, among others) it into useful information, conducting pre-diagnosis and providing, when necessary, alert signs to the medical staff.ConclusionsThe use of fuzzy logic turned to the medical area can be very useful if seen as a tool to assist specialists in this area. This paper presented a fuzzy model able to monitor and classify the condition of the vital signs of hospitalized patients, sending alerts according to the pre-diagnosis done helping the medical diagnosis.

BackgroundAcross the globe, breast cancer is one of the leading causes of death among women and, currently, Fine Needle Aspirate (FNA) with visual interpretation is the easiest and fastest biopsy technique for the diagnosis of this deadly disease. Unfortunately, the ability of this method to diagnose cancer correctly when the disease is present varies greatly, from 65% to 98%. This article introduces a method to assist in the diagnosis and second opinion of breast cancer from the analysis of descriptors extracted from smears of breast mass obtained by FNA, with the use of computational intelligence resources - in this case, fuzzy logic.MethodsFor data acquisition of FNA, the Wisconsin Diagnostic Breast Cancer Data (WDBC), from the University of California at Irvine (UCI) Machine Learning Repository, available on the internet through the UCI domain was used. The knowledge acquisition process was carried out by the extraction and analysis of numerical data of the WDBC and by interviews and discussions with medical experts. The PDM-FNA-Fuzzy was developed in four steps: 1) Fuzzification Stage; 2) Rules Base; 3) Inference Stage; and 4) Defuzzification Stage. Performance cross-validation was used in the tests, with three databases with gold pattern clinical cases randomly extracted from the WDBC. The final validation was held by medical specialists in pathology, mastology and general practice, and with gold pattern clinical cases, i.e. with known and clinically confirmed diagnosis.ResultsThe Fuzzy Method developed provides breast cancer pre-diagnosis with 98.59% sensitivity (correct pre-diagnosis of malignancies); and 85.43% specificity (correct pre-diagnosis of benign cases). Due to the high sensitivity presented, these results are considered satisfactory, both by the opinion of medical specialists in the aforementioned areas and by comparison with other studies involving breast cancer diagnosis using FNA.ConclusionsThis paper presents an intelligent method to assist in the diagnosis and second opinion of breast cancer, using a fuzzy method capable of processing and sorting data extracted from smears of breast mass obtained by FNA, with satisfactory levels of sensitivity and specificity. The main contribution of the proposed method is the reduction of the variation hit of malignant cases when compared to visual interpretation currently applied in the diagnosis by FNA. While the MPD-FNA-Fuzzy features stable sensitivity at 98.59%, visual interpretation diagnosis provides a sensitivity variation from 65% to 98% (this track showing sensitivity levels below those considered satisfactory by medical specialists). Note that this method will be used in an Intelligent Virtual Environment to assist the decision-making (IVEMI), which amplifies its contribution.

Resumo: Entre os testes da triagem neonatal mais comuns tem-se: orelhinha, olhinho, pezinho, coraçãozinho e linguinha, em que são avaliadas as condições auditiva, visual, sanguínea, de saturação de oxigênio e de movimentação da língua, respectivamente. O contexto deste trabalho refere-se ao teste da linguinha, que verifica se o recém-nascido consegue movimentar corretamente a língua, pois se houver algo obstruindo essa movimentação, o bebê apresentará dificuldades nas funções que ela exerce (engolir, sugar, falar, mastigar, etc.). Sabe-se que identificar problemas na movimentação da língua não é fácil, assim, o auxílio de sistemas computacionais e de processamento inteligente das informações contribuiriam muito para acompanhar e dar suporte aos profissionais de saúde responsáveis pelo teste. Com isso, este trabalho tem como objetivo desenvolver um sistema de apoio à decisão na realização e no acompanhamento do teste da linguinha, utilizando redes neurais artificiais para emissão de alertas e recomendações em situações anormais. Destaca-se que, na concepção da rede neural artificial, foi utilizada uma base de dados criada por meio de protocolos reais fornecidos pelos especialistas que acompanharam o trabalho. Os resultados apresentados foram considerados satisfatórios e relevantes por médicos especialistas, principalmente pelas possibilidades de melhoria no atendimento dos recém-nascidos que pode ser propiciado pelo uso do sistema. (swallowing, sucking, talking, chewing, etc.) Palavras-chave: Assistência neonatal. Redes neurais artificiais. Sistema de apoio à decisão. Abstract: Among the most common newborn screening tests we have: ears, eyes, blood, heart and tongue, which evaluates hearing condition, visual condition, blood condition, oxygen saturation and tongue movement, respectively. The context of this work will be the tongue test where it is important to check that the newborn can properly move his tongue, as if he has something obstructing this movement, will present difficulties in the functions which it exercises

Information generated by sensors that collect a patient's vital signals are continuous and unlimited data sequences. Traditionally, this information requires special equipment and programs to monitor them. These programs process and react to the continuous entry of data from different origins. Thus, the purpose of this study is to analyze the data produced by these biomedical devices, in this case the electrocardiogram (ECG). Processing uses a neural classifier, Kohonen competitive neural networks, detecting if the ECG shows any cardiac arrhythmia. In fact, it is possible to classify an ECG signal and thereby detect if it is exhibiting or not any alteration, according to normality.

Currently, Diabetes is a very common disease around the world, and with an increase in sedentary lifestyles, obesity and an aging population the number of people with Diabetes worldwide will increase by more than 50%. In this context, the MIT (Massachusetts Institute of Technology) developed the SANA platform, which brings the benefits of information technology to the field of healthcare. It offers healthcare delivery in remote areas, improves patient access to medical specialists for faster, higher quality, and more cost effective diagnosis and intervention. For these reasons, we developed a system for diagnosis of Diabetes using the SANA platform, called S2DIA. It is the first step towards knowing the risks for type 2 Diabetes, and it will be evaluated, especially, in remote/poor areas of Brazil.