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.

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.

Due to the need for management, control, and monitoring of information in an effient way. The hospital automation has been the object of a number of studies owing to constantly evolving technologies. However, many hospital processes are still manual in private and public hospitals. Thus, the aim of this study is to model and simulate of medical care provided to patients in the Intensive Care Unit (ICU), using stochastic Petri Nets and their possible use in a number of automation processes.

Resumo Em função de dispersão geográfica (por se encontrarem em diferentes hospitais, cidades ou países) e/ou problemas de conciliação de tempo, os profissionais da área de saúde (médicos e outros) têm dificuldade de sincronização espaço-temporal para realização de trabalho conjunto. Entretanto, existe uma crescente demanda para a realização de trabalho colaborativo envolvendo vários profissionais para análise da situação de um paciente ou sua confirmação (diagnóstico ou segunda opinião). Por outro lado, também é crescente a demanda pela utilização de inteligência computacional no apoio à tomada de decisão na área de saúde. Inserindo-se nesse contexto, o objetivo deste trabalho foi o desenvolvimento de um ambiente de telediagnóstico colaborativo, com a incorporação de recursos de inteligência computacional em módulos especialistas responsáveis pela análise de exames e dados clínicos específicos do paciente, visando prover um pré-diagnóstico. O modelo desenvolvido, operando tanto de forma síncrona (usuários conectados no mesmo instante de tempo) quanto assíncrona (usuários interagindo em momentos distintos), é capaz de: i) prover o suporte ao trabalho colaborativo a distância de múltiplos usuários (equipe médico-hospitalar); ii) disponibilizar aos usuários informações gráficas e textuais; e iii) incorporar módulos inteligentes que, utilizando técnicas de inteligência computacional, são capazes de realizar pré-diagnósticos a partir de dados e exames de pacientes.Palavras-chave Telemedicina, Telediagnóstico, Trabalho colaborativo a distância, Inteligência computacional, Sistema de apoio à decisão, Automação hospitalar. Collaborative remote diagnostics environment using intelligent platform support decisionAbstract Due to geographical dispersion (for without are in different hospitals, city or country) and/or time problems, health professionals, particularly physicians, have difficulty synchronizing space and time to perform joint work. However, there is growing demand for collaborative work involving several professionals to analyze the situation of a patient or its confirmation (diagnosis or second opinion). On the other hand, there is also a growing demand for the use of computational intelligence to support decision making in health care. In that context, the objective of this work is to develop a collaborative environment for telediagnosis, with the incorporation of computational intelligence resources into specialist modules responsible for testing the patient's exams and specific clinical data, to provide a pre-diagnosis. The model developed, operating both synchronously (users connected at the same instant of time) or asynchronously (users interacting at different times), is capable of: i) provide support for collaborative work away multiple users (healthcare); ii) provide users with graphical and textual information; and iii) incorporate specialistas modules using computational intelligence techniques, are able to perform pre-diagnostics from data and examinations of patients.

Some diseases, such as hypertension, require a close control of the patient's blood pressure. This is even more critical when that patient is going through--or has just underwent--a surgical procedure In such situations, reducing blood pressure to normal levels is of paramount importance. Usually, this demanding and time consuming monitoring is done manually by clinical personnel and are subject to mistakes and inconsistent practices. In this paper, we propose a solution to the manual monitoring through the design and implementation of an embedded PID controller to handle blood pressure, integrated to an automated monitoring system to assist in detecting anomalies and to optimize the process of patient care.