A hybrid linear discriminant analysis and genetic algorithm to create a linear model of aging when performing motor tasks through inertial sensors positioned on the hand and forearm.

BACKGROUND: During the aging process, cognitive functions and performance of the muscular and neural system show signs of decline, thus making the elderly more susceptible to disease and death. These alterations, which occur with advanced age, affect functional performance in both the lower and upper members, and consequently human motor functions. Objective measurements are important tools to help understand and characterize the dysfunctions and limitations that occur due to neuromuscular changes related to advancing age. Therefore, the objective of this study is to attest to the difference between groups of young and old individuals through manual movements and whether the combination of features can produce a linear correlation concerning the different age groups. METHODS: This study counted on 99 participants, these were divided into 8 groups, which were grouped by age. The data collection was performed using inertial sensors (positioned on the back of the hand and on the back of the forearm). Firstly, the participants were divided into groups of young and elderly to verify if the groups could be distinguished through the features alone. Following this, the features were combined using the linear discriminant analysis (LDA), which gave rise to a singular feature called the LDA-value that aided in verifying the correlation between the different age ranges and the LDA-value. RESULTS: The results demonstrated that 125 features are able to distinguish the difference between the groups of young and elderly individuals. The use of the LDA-value allows for the obtaining of a linear model of the changes that occur with aging in the performance of tasks in line with advancing age, the correlation obtained, using Pearson's coefficient, was 0.86. CONCLUSION: When we compare only the young and elderly groups, the results indicate that there is a difference in the way tasks are performed between young and elderly individuals. When the 8 groups were analyzed, the linear correlation obtained was strong, with the LDA-value being effective in obtaining a linear correlation of the eight groups, demonstrating that although the features alone do not demonstrate gradual changes as a function of age, their combination established these changes.

Systematic error compensation for improving accuracy of the electrosurgical unit.

This paper presents an approach to systematic error correction for improving the accuracy of the electrosurgical unit (ESU), through a method that enables safer operation for both surgeons and patients. The equations for systematic errors encountered during the calibration of the eight ESUs were obtained using regression analysis. The ESU calibration was performed using a power analyzer and the reference power value was selected on the ESU panel; the power value indicated by the ESU was noted from the power analyzer and recorded. The fitted regression models were suitable for predicting systematic errors associated with ESU output power values, as a reduction of about 84 % was noted in cutting power (CuP) and about 74 % for coagulation power (CoP). After correction, systematic error values remained below 1.9 W and 0.4 W in cutting and coagulation power, respectively. The maximum error values obtained were less than those specified for the IEC 60601-2-2 standard for all evaluated ESUs.

A model for multidimensional efficiency analysis of public hospital management

Abstract Introduction Multidimensional efficiency analysis can provide important insights into the performance of hospitals. In this paper, we propose a multidimensional model based on Data Envelopment Analysis (DEA) to investigate and compare the efficiency of public hospitals in Brazil. Methods Data from 21 public hospitals were collected from public databases (OECD - Organization for Economic Co-operation and Development; SIH-SUS - SUS Hospital Information System, Datasus, Brazil). Four inputs (Number of medical and non-medical staff, Annual revenue, Number of beds, Average length of patient hospitalization), four Variables of Influence (Type of hospital, Accredited hospital, Number of medical specialties, Resources from government) and four Outputs (Number of outpatient care services, Number of hospitalizations, Number of surgeries, Number of exams) were used to feed the DEA model. Results Seven hospital units reach 100% efficiency and, according to DEA, can be considered efficient units. Two units were considered "almost efficient" and the remaining twelve units perform poorly, considering the data supplied to the DEA model. As a whole, the average efficiency of the hospitals investigated was 79% (0.79). Conclusion A very heterogeneous performance has been found among the Brazilian public hospitals investigated. Besides, the reasonably low average efficiency seems to indicate that the system has a large potential for improvement in almost all areas associated with the input and output variables investigated in this paper.

An investigative case study at local hospital into the current reliability of blood pressure sphygmomanometers

Abstract Introduction Arterial Blood Pressure is a significant indicator of the current health condition of an individual. The correct detection of hypertension is essential, where this health problem is considered as one of the greatest health risks factors that affect the heart and circulatory system. This paper presents the importance of the application of metrological criteria for the diagnosis of hypertension using a sphygmomanometer aneroid. Methods 72 mechanical aneroid sphygmomanometers were calibrated using a standard manometer and the indication error, hysteresis, air leakage and rapid exhaust were determined; readings of these sphygmomanometers were compared to a properly calibrated and adjusted aneroid sphygmomanometer to carry out pressure measurements as those made during the hypertension diagnosis; the uncertainty of measurement associated with the sphygmomanometers calibration, and pressure values was assessed according to the recommendations of the Guide to the Expression of Uncertainty in Measurement, defined by the Joint Committee for Guides in Metrology. Results The results obtained have shown that about 61% of the evaluated aneroid sphygmomanometers did not meet the specifications. The variable that most contributed to the final calibration uncertainty was the hysteresis of the standard manometer, with 53% of contribution, followed by the sphygmomanometer resolution with 27%. Conclusion The periodic verifications are essential to evaluate the performance of these devices. It was shown that uncertainty of measurement influences the final diagnosis of hypertension and the application of metrological criteria can increase the reliability of the final diagnosis.

Avaliação econômica da implantação de um serviço de Engenharia Clínica em hospital público brasileiro / Economic evaluation of implementing a service of Clinical Engineering in a Brazilian public hospital

A Engenharia Clínica é uma área com atuação nos Estabelecimentos Assistenciais de Saúde, desenvolvendo atividades baseadas nos conhecimentos de engenharia e de gerenciamento aplicadas às tecnologias de saúde. Nesse contexto, este trabalho relata os impactos econômicos da aplicação de técnicas de gestão em Engenharia Clínica num hospital público brasileiro de grande porte e referência em alta complexidade - Hospital de Clínicas da Universidade Federal de Uberlândia. Trata-se de uma pesquisa quantitativa, onde foram consideradas questões econômicas e de qualidade, suportada por pesquisa documental. O levantamento de dados baseou-se em relatórios e documentos administrativos do hospital do período de 2001 a 2010, referentes a recursos humanos, indicadores de serviços, indicadores de qualidade, custos com peças e contratos. Dentre os resultados encontrados, destaca-se a redução de aproximadamente 20% nas manutenções corretivas e sua estabilização ao longo do tempo, mesmo em face de um elevado aumento do parque tecnológico da instituição. No montante global de gastos com contratos, observou-se uma redução de cerca de 65% no período avaliado. A economia gerada pela gestão em Engenharia Clínica para a instituição foi de aproximadamente R$ 2 milhões em 2010, e a economia acumulada no período de 2001 a 2010 foi de R$ 7,6 milhões. Concluiu-se, que a gestão em Engenharia Clínica na instituição possibilitou uma redução significativa nos custos por meio da formação e capacitação de uma equipe própria, da redução dos custos com contratos e melhor planejamento das manutenções. Tais resultados demonstram a importância de um serviço de Engenharia Clínica para a melhor gestão de custos e das tecnologias em hospitais, sejam eles públicos ou privados.

Clinical Engineering is an important area for health care facilities, capable of applying engineering and management techniques to improve health technologies. In this context, this paper reports the economic impacts of the application of clinical engineering management techniques in a large Brazilian public hospital, which is also a reference for high complexity medical procedures - General Hospital of the Federal University of Uberlândia. The discussion is supported by a quantitative documentary research, which took into account not only the economic aspects, but also the quality of the service provided. The survey was based on reports and administrative documents from 2001 to 2010, related to human resources, service and quality indicators, costs of parts and contracts. Among the findings, it was observed a reduction of approximately 20% in corrective maintenance and their stabilization over time, even as the technological park of the institution increased. As for the overall amount of cost with contracts, there was a reduction of approximately 65% during the period. The savings generated by the Clinical Engineering Service, for the institution, were about R$ 2 million in 2010. The cumulative savings over the period of 2001 to 2010 were about R$ 7.6 million. Based on those results, it can be concluded that the Clinical Engineering Service provided a significant reduction in costs for the institution, by means of setting and training its own team, reducing costs and a better planning of maintenance. Those results demonstrate the importance of a Clinical Engineering Service for best managing costs and technologies in hospitals, whether public or private.

[Use of an artificial neural network to predict the incidence of malaria in the city of Cantá, state of Roraima]. / A utilização de uma rede neural artificial para previsão da incidência da malária no município de Cantá, estado de Roraima.

INTRODUCTION: Malaria is endemic in the Brazilian Amazon region, with different risks for each region. The City of Cantá, State of Roraima, presented one of the largest annual parasite indices in Brazil for the entire study period, with a value always greater than 50. The present study aimed to use an artificial neural network to predict the incidence of malaria in this city in order to assist health coordinators in planning and managing resources. METHODS: Data were collected on the website of the Ministry of Health, SIVEP--Malaria between 2003 and 2009. An artificial neural network was structured with three neurons in the input layer, two intermediate layers and an output layer with one neuron. A sigmoid activation function was used. In training, the backpropagation method was used, with a learning rate of 0.05 and momentum of 0.01. The stopping criterion was to reach 20,000 cycles or a target of 0.001. The data from 2003 to 2008 were used for training and validation. The results were compared with those from a logistic regression model. RESULTS: The results for all periods provided showed that the artificial neural network had a smaller mean square error and absolute error compared with the regression model for the year 2009. CONCLUSIONS: The artificial neural network proved to be adequate for a malaria forecasting system in the city studied, determining smaller predictive values with absolute errors compared to the logistic regression model and the actual values.

A utilização de uma rede neural artificial para previsão da incidência da malária no município de Cantá, estado de Roraima / Use of an artificial neural network to predict the incidence of malaria in the city of Cantá, state of Roraima

INTRODUÇÃO: A malária é uma doença endêmica na Amazônia Legal Brasileira, apresentando riscos diferentes para cada região. O Município de Cantá, no Estado de Roraima, apresentou para todo o período estudado, um dos maiores índices parasitários anuais do Brasil, com valor sempre maior que 50. O presente estudo visa à utilização de uma rede neural artificial para previsão da incidência da malária nesse município, a fim de auxiliar os coordenadores de saúde no planejamento e gestão dos recursos. MÉTODOS: Os dados foram coletados no site do Ministério da Saúde, SIVEP - Malária entre 2003 e 2009. Estruturou-se uma rede neural artificial com três neurônios na camada de entrada, duas camadas intermediárias e uma camada de saída com um neurônio. A função de ativação foi à sigmoide. No treinamento, utilizou-se o método backpropagation, com taxa de aprendizado de 0,05 e momentum 0,01. O critério de parada foi atingir 20.000 ciclos ou uma meta de 0,001. Os dados de 2003 a 2008 foram utilizados para treinamento e validação. Comparam-se os resultados com os de um modelo de regressão logística. RESULTADOS: Os resultados para todos os períodos previstos mostraram-se que as redes neurais artificiais obtiveram um menor erro quadrático médio e erro absoluto quando comparado com o modelo de regressão para o ano de 2009. CONCLUSÕES: A rede neural artificial se mostrou adequada para um sistema de previsão de malária no município estudado, determinando com pequenos erros absolutos os valores preditivos, quando comparados ao modelo de regressão logística e aos valores reais.

INTRODUCTION: Malaria is endemic in the Brazilian Amazon region, with different risks for each region. The City of Cantá, State of Roraima, presented one of the largest annual parasite indices in Brazil for the entire study period, with a value always greater than 50. The present study aimed to use an artificial neural network to predict the incidence of malaria in this city in order to assist health coordinators in planning and managing resources. METHODS: Data were collected on the website of the Ministry of Health, SIVEP - Malaria between 2003 and 2009. An artificial neural network was structured with three neurons in the input layer, two intermediate layers and an output layer with one neuron. A sigmoid activation function was used. In training, the backpropagation method was used, with a learning rate of 0.05 and momentum of 0.01. The stopping criterion was to reach 20,000 cycles or a target of 0.001. The data from 2003 to 2008 were used for training and validation. The results were compared with those from a logistic regression model. RESULTS: The results for all periods provided showed that the artificial neural network had a smaller mean square error and absolute error compared with the regression model for the year 2009. CONCLUSIONS: The artificial neural network proved to be adequate for a malaria forecasting system in the city studied, determining smaller predictive values with absolute errors compared to the logistic regression model and the actual values.