33 resultados para Neoplasias da Próstata - Classificação

em Universidade Federal do Rio Grande do Norte(UFRN)


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PEREIRA, Edinete do Nascimento et al. Classificação bibliográfica: as diversas contribuições para o tratamento da informação. In: SEMINÁRIO DE PESQUISA DO CCSA, 15., 2009. Anais... Natal: UFRN, 2009.

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This master dissertation presents the study and implementation of inteligent algorithms to monitor the measurement of sensors involved in natural gas custody transfer processes. To create these algoritmhs Artificial Neural Networks are investigated because they have some particular properties, such as: learning, adaptation, prediction. A neural predictor is developed to reproduce the sensor output dynamic behavior, in such a way that its output is compared to the real sensor output. A recurrent neural network is used for this purpose, because of its ability to deal with dynamic information. The real sensor output and the estimated predictor output work as the basis for the creation of possible sensor fault detection and diagnosis strategies. Two competitive neural network architectures are investigated and their capabilities are used to classify different kinds of faults. The prediction algorithm and the fault detection classification strategies, as well as the obtained results, are presented

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Stroke represents the first cause of disabilities among adults. Although different professions work together in treatment of stroke patients, all they use different terminologies for the description of the patients problems and it can constitute an impediment in the communication between the staff members. Thus, the multidisciplinary and interdisciplinary work would be facilitated if using a reference common tool, as the new International Classification of Functioning, Disability and Health (ICF). However, the ICF is very extensive and complex and due to its complexity, it has been evidenced the necessity to select its categories to become it more practical. The aim of the study was to investigate which categories of the ICF are more suitable to evaluate and to describe the stroke patient in the view of teachers and municipal public health professionals. It was a descriptive research, which involved 5 professors and 11 professionals of Physiotherapy that have worked at the health public area in Natal / RN. It was used the Delphi Technique in 3 rounds and the Likert Scale to select the categories among the ICF components. As result, from the 362 IFC categories, 94 were selected. The selected categories correspond to rehabilitative characteristics of Stroke patients in the universe of the Physiotherapy performance. The methodology applied was suitable to the studied object emphasizing the necessity of future studies for validation of the chosen categories

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Foi avaliada, no presente estudo, a prevalência dos casos de tumores benignos e malignos de glândulas salivares maiores e menores que ocorreram na população atendida no hospital Dr. Luiz Antônio Natal/RN, no período de 1989 a 2005, bem como as possíveis associações existentes entre os aspectos clínico e patológicos exibidos pelos referidos tumores, visando à obtenção de parâmetros indicadores de diagnóstico e/ou prognóstico. Dos prontuários dos pacientes foram obtidas todas as informações clínicas necessárias para a realização do trabalho. A análise dos dados revelou que dos 303 tumores de glândula salivar estudados, a maioria (71%) foram benignos, o mais comum foi adenoma Pleomórfico. As médias de idades para os tumores benignos e malignos foi de 49,2 e 58,5 anos, respectivamente. Diferenças estatisticamente significativas entre estes tumores foram observadas para as seguintes variáveis: idade média, o tamanho do tumor e duração da doença. Em relação ao tamanho do tumor, carcinoma mucoepidermoide mostrou-se 1,74 vezes menor que o de outros tumores malignos. Uma associação entre o diagnóstico histológico e variável consistência do tumor foi observada. Os dados apresentados neste estudo são relevantes para a compreensão das diversas características exibidas por estes tumores, já que corroboram uma série de estudos anteriores

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The acute myeloid leukemia (AML) is a disease in which malignant myeloblasts expand, build up and suppress normal hematopoietic activity would represent a major diagnostic challenge. With the advent of immunophenotyping by flow cytometry, the diagnosis of these tumors have become more faithful, facilitating the treatment and monitoring of patients. The objectives of this study: diagnosis and classification of AML based on immunophenotyping by flow cytometry with a panel of AcMo specific for acute leukemias, set the frequency of AML in samples from patients with acute leukemias sent to the Department of Hematology Blood Center of Rio Grande do Norte - HEMONORTE, establish standards of antigen expression for different subtypes of acute leukemia and its correlation with the newly diagnosed cases refractory to treatment and recurrence of the disease, standardization of methods for detection and labeling of surface antigens by flow cytometry and intracytoplasmic flow, and observe the frequency of acute leukemia with aberrant phenotypes rare. During the study, 351 were diagnosed acute leukemia, and 179 (51%) classified as AML and 172 (49%) and ALL, which were excluded from the present work. Of the 179 AML, 92 (51.4%) were female and 87 (48.6%) were male, with ages ranging from 3 to 95 years of ag, with higher incidence in individuals in the age group of 41 to 65. Splenomegaly was the clinical finding more present, a total of 147 cases (82.1%), followed by hepatomegaly present in 132 cases (73.7%). The hemorrhagic events were observed in 55 cases (30.7%). Lymphadenopathy in turn was detected in 20 of 179 cases (11.2%). In order to classify subtypes of AML, we used a large panel of monoclonal antibodies, obtaining the following results: AML M0, 02 (1.1%) AML M1, 40 (22.3) AML M2, 60 (33.5) AML M3, 22 (12.3%) AML M4, 10 (5.6) AML M5, 13 (7.3%) AML M6 06 (3.4%) and AML M7 01 (0.6%). We observed some cases with aberrant expression of some antigens such as CD7, CD4, CD19, CD3, CD5 and TdT, CD 7 was present in 30 (16.8%), CD4 in 5 (2.8%), the CD 3 in 5 (2.8%), the CD19 in 3 (1.7%), the CD5 in 3 (1.7%) and TDT was in 7 (3.9%) cases of AML .the CD8 and CD79a was present in only a 1 case.

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Leukemia is a heterogeneous group of hematologic malignancies that result from partial or total transformation of the blast cells. The Acute Lymphoblastic Leukemia (ALL) is the most common malignancy in childhood, especially in male, Caucasian children younger than 14 years. Several criteria are adopted to classify ALL, including the cell morphology, cytochemistry, immunophenotyping and cytogenetic analysis. Cytogenetic studies allow a more detailed analysis to detect chromosomal abnormalities of leukemic cells. These modifications will determine the diagnosis, classification, stage characterization, remission assessment and prognosis. In this study were evaluated 30 patients, aged from four months to seventeen years, of both sexes and various ethnicities. The age distribution showed that 67% of patients had between one and ten years (with mean age of XX years old), the most prevalent ethnic was Caucasian (50%) and 57% were males. According to immunophenotype, 93% of patients had B-cells progenitor ALL and 7% early lineage T. Considering the total studied population, the most frequent medical findings were lymphadenopathy (37%), hepatomegaly (77%) and splenomegaly (70%), where one patient could present more than one of these medical findings. Regarding the CBC, the majority of patients had hemoglobin below 10 g / dl (73%), leukocyte count less than 10.000/μL (60%) and platelet count below 150.000/μL (83%). Chromosomal abnormalities were observed in 64% of all patients, where hyperdiploidy was the most common numerical change (67%), followed by hypodiploid (33%). All these data are in agreement with the literature. Moreover, complexes structural and/or number changes not yet described in literature were observed, which indicated poor prognosis. Finally, we concluded that this study demonstrated the importance of cytogenetic study in the diagnosis and identification of prognostic factors in pediatric patients with ALL in Rio Grande do Norte. The results obtained in this study are extremely useful and emphasizes that surveys of this nature must be conducted more frequently in our state

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The venous ulcer is an epidemiological problem of high prevalence, causing disability and dependence. Assess the tissue impairment level of patients with venous lesions, within a nursing referential, is relevant for the implementation of a directed assistance to specific clientele. Thus, this work aims to characterize the health status regarding the integrity the lower limbs skin of patients with venous ulcers, according to the of tissue integrity outcome indicators from the Nursing Outcomes Classification. A cross-sectional study conducted in a university hospital in Natal - Rio Grande do Norte. The sample consisted of 50 participants, selected through consecutive sampling. Data collection occurred through a interview and physical examination form and a operational definitions tool for indicators of the nursing Tissue Integrity outcome directed to patients with venous ulcer, applied from February to June 2012. Data analysis was done by descriptive statistics and nonparametric tests (Spearman, Kruskal-Wallis and Mann-Whitney tests). The project was approved by the Research Ethics Committee with protocol 608/11 and Presentation Certificate to Ethical Consideration No. 0038.0.294.000-11. The results were presented using three scientific articles derivatives of research. It was found that the indicators show moderate impairment, light and not impaired, as the median. The respondents had an average of 59.72 years, 66% female, 50% were retired, 60% with a partner, 44% had arterial hypertension, 26% allergies, 20% diabetes mellitus, 96% were sedentary, 14% drank alcohol and 6% were smokers. There was a statistically significant correlation of low intensity between age and hydration (p=0.032; rs=-0.304) and skin desquamation (p=0.026; rs=-0.316), family income and necrosis (p=0.012; rs=-0.353); Ankle Brachial Index and tissue perfusion (p=0,044; rs=-0,329); Diabetes Mellitus and texture (p=0.015) and tissue perfusion (p=0.026); allergy and texture (p=0.034), physical activity and hydration (p=0.034), smoking and thickness (p=0.018), and alcohol consumption and exudate (p=0.045). We conclude that the patients had light to moderate impairment, indicating a good state of health on the integrity of the skin of the lower limbs, according to the indicators of the outcome of tissue integrity Classification Nursing Outcomes valued in the present study. It is believed that the evaluation of impairment tissue using a self-nursing system and its relation with socioeconomic, clinical and risk factors are unique tools in the care planning and in the wound healing

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The objective of this work is to draw attention to the importance of use of techniques of loss prevention in small retail organization, analyzing and creating a classification model related to the use of these in companies. This work identifies the fragilities and virtues of companies and classifies them relating the use of techniques of loss prevention. The used methodology is based in a revision of the available literature on measurements and techniques of loss prevention, analyzing the processes that techniques needed to be adopted to reduce losses, approaching the "pillars" of loss prevention, the cycle life of products in retail and cycles of continues improvement in business. Based on the objectives of this work and on the light of researched techniques, was defined the case study, developed from a questionnaire application and the researcher's observation on a net of 16 small supermarkets. From those studies a model of classification of companies was created. The practical implications of this work are useful to point mistakes in retail administration that can become losses, reducing the profitability of companies or even making them impracticable. The academic contribution of this study is a proposal of an unpublished model of classification for small supermarkets based on the use of techniques of loss prevention. As a result of the research, 14 companies were classified as Companies with Minimum Use of Loss Prevention Techniques - CMULPT, and 02 companies were classified as Companies with Deficient Use of Loss Prevention Techniques - CDULPT. The result of the research concludes that on average the group was classified as being Companies with Minimum Use of Techniques of Prevention of Losses EUMTPP, and that the companies should adopt a program of loss prevention focusing in the identification and quantification of losses and in a implantation of a culture of loss prevention

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Este trabalho tem como objetivo estudar os sistemas de Classificações existentes para a garantia da gestão da qualidade no setor hoteleiro, tendo como foco principal a Matriz de Classificação para os Meios de Hospedagem da EMBRATUR e a ISO 9000, observando os benefícios que esses sistemas e/ou processos de gestão poderão vir a proporcionar para o setor hoteleiro no que se refere à qualidade de seus serviços. Para a obtenção dessas informações foi realizada uma análise comparativa dos sistemas de gestão da qualidade através de pesquisas bibliográficas e de questionários enviados para empreendimentos hoteleiros certificados e classificados, onde os principais resultados fornecidos pela pesquisa foram trabalhados de forma a apresentar, de maneira clara, a superioridade de um sistema em relação ao outro

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The use of the maps obtained from remote sensing orbital images submitted to digital processing became fundamental to optimize conservation and monitoring actions of the coral reefs. However, the accuracy reached in the mapping of submerged areas is limited by variation of the water column that degrades the signal received by the orbital sensor and introduces errors in the final result of the classification. The limited capacity of the traditional methods based on conventional statistical techniques to solve the problems related to the inter-classes took the search of alternative strategies in the area of the Computational Intelligence. In this work an ensemble classifiers was built based on the combination of Support Vector Machines and Minimum Distance Classifier with the objective of classifying remotely sensed images of coral reefs ecosystem. The system is composed by three stages, through which the progressive refinement of the classification process happens. The patterns that received an ambiguous classification in a certain stage of the process were revalued in the subsequent stage. The prediction non ambiguous for all the data happened through the reduction or elimination of the false positive. The images were classified into five bottom-types: deep water; under-water corals; inter-tidal corals; algal and sandy bottom. The highest overall accuracy (89%) was obtained from SVM with polynomial kernel. The accuracy of the classified image was compared through the use of error matrix to the results obtained by the application of other classification methods based on a single classifier (neural network and the k-means algorithm). In the final, the comparison of results achieved demonstrated the potential of the ensemble classifiers as a tool of classification of images from submerged areas subject to the noise caused by atmospheric effects and the water column

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The skin cancer is the most common of all cancers and the increase of its incidence must, in part, caused by the behavior of the people in relation to the exposition to the sun. In Brazil, the non-melanoma skin cancer is the most incident in the majority of the regions. The dermatoscopy and videodermatoscopy are the main types of examinations for the diagnosis of dermatological illnesses of the skin. The field that involves the use of computational tools to help or follow medical diagnosis in dermatological injuries is seen as very recent. Some methods had been proposed for automatic classification of pathology of the skin using images. The present work has the objective to present a new intelligent methodology for analysis and classification of skin cancer images, based on the techniques of digital processing of images for extraction of color characteristics, forms and texture, using Wavelet Packet Transform (WPT) and learning techniques called Support Vector Machine (SVM). The Wavelet Packet Transform is applied for extraction of texture characteristics in the images. The WPT consists of a set of base functions that represents the image in different bands of frequency, each one with distinct resolutions corresponding to each scale. Moreover, the characteristics of color of the injury are also computed that are dependants of a visual context, influenced for the existing colors in its surround, and the attributes of form through the Fourier describers. The Support Vector Machine is used for the classification task, which is based on the minimization principles of the structural risk, coming from the statistical learning theory. The SVM has the objective to construct optimum hyperplanes that represent the separation between classes. The generated hyperplane is determined by a subset of the classes, called support vectors. For the used database in this work, the results had revealed a good performance getting a global rightness of 92,73% for melanoma, and 86% for non-melanoma and benign injuries. The extracted describers and the SVM classifier became a method capable to recognize and to classify the analyzed skin injuries

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Post dispatch analysis of signals obtained from digital disturbances registers provide important information to identify and classify disturbances in systems, looking for a more efficient management of the supply. In order to enhance the task of identifying and classifying the disturbances - providing an automatic assessment - techniques of digital signal processing can be helpful. The Wavelet Transform has become a very efficient tool for the analysis of voltage or current signals, obtained immediately after disturbance s occurrences in the network. This work presents a methodology based on the Discrete Wavelet Transform to implement this process. It uses a comparison between distribution curves of signals energy, with and without disturbance. This is done for different resolution levels of its decomposition in order to obtain descriptors that permit its classification, using artificial neural networks

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The precision and the fast identification of abnormalities of bottom hole are essential to prevent damage and increase production in the oil industry. This work presents a study about a new automatic approach to the detection and the classification of operation mode in the Sucker-rod Pumping through dynamometric cards of bottom hole. The main idea is the recognition of the well production status through the image processing of the bottom s hole dynamometric card (Boundary Descriptors) and statistics and similarity mathematics tools, like Fourier Descriptor, Principal Components Analysis (PCA) and Euclidean Distance. In order to validate the proposal, the Sucker-Rod Pumping system real data are used

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The Brain-Computer Interfaces (BCI) have as main purpose to establish a communication path with the central nervous system (CNS) independently from the standard pathway (nervous, muscles), aiming to control a device. The main objective of the current research is to develop an off-line BCI that separates the different EEG patterns resulting from strictly mental tasks performed by an experimental subject, comparing the effectiveness of different signal-preprocessing approaches. We also tested different classification approaches: all versus all, one versus one and a hierarchic classification approach. No preprocessing techniques were found able to improve the system performance. Furthermore, the hierarchic approach proved to be capable to produce results above the expected by literature

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Reinforcement learning is a machine learning technique that, although finding a large number of applications, maybe is yet to reach its full potential. One of the inadequately tested possibilities is the use of reinforcement learning in combination with other methods for the solution of pattern classification problems. It is well documented in the literature the problems that support vector machine ensembles face in terms of generalization capacity. Algorithms such as Adaboost do not deal appropriately with the imbalances that arise in those situations. Several alternatives have been proposed, with varying degrees of success. This dissertation presents a new approach to building committees of support vector machines. The presented algorithm combines Adaboost algorithm with a layer of reinforcement learning to adjust committee parameters in order to avoid that imbalances on the committee components affect the generalization performance of the final hypothesis. Comparisons were made with ensembles using and not using the reinforcement learning layer, testing benchmark data sets widely known in area of pattern classification