Establishment of Rational Drug Use Evaluation Model Based on 360-degree Feedback Evaluation Method and Hesitant Fuzzy Sets Theory / 中国药房

OBJECTIVE： To establish a comprehensive and multi-angle evaluation model for rational drug use in medical institutions， and to provide support for rational drug use in medical institutions. METHODS： Referring to the phenomenon of irrational drug use and its influential factors in medical institutions， rational drug use evaluation indicator system， which contained 3 evaluation subjects， 6 first-level indicators and 15 second-level indicators， was established on the basis of 360-degree Feedback Method， with hospitals at the same level， superior regulation department and patient as subjects of multi-angle evaluation. At the same time， the linguistic information of vague and uncertain evaluation given by the evaluation subject was comprehensively quantified by Hesitant Fuzzy Sets Theory. The total evaluation score of rational drug use in the evaluated medical institutions was calculated by using the score function formula of hesitant fuzzy number； the level of rational drug use in medical institutions was determined according to the score （the higher the total score， the higher the comprehensive level of rational drug use）. RESULTS： An all-round and multi-angle evaluation model of rational drug use in medical institutions was established. According to the evaluation index system based on 360-degree feedback evaluation method， the evaluation subject and weight of evaluation index were determined， the grade of evaluation index was divided， the hesitant fuzzy numbers of each index were gathered， and the first-level index was weighted. Finally， the total evaluation score of rational drug use in medical institutions was calculated； the examples were set to prove the feasibility of the model. CONCLUSIONS： The established evaluation model for rational drug use in medical institutions based on 360-degree Feedback Method and Hesitant Fuzzy Sets Theory can comprehensively and effectively evaluate the rational drug use in medical institutions， and contribute to standardizing and improving clinical drug use behavior in medical institutions.

Identification of certain cancer-mediating genes using Gaussian fuzzy cluster validity index.

In this article, we have used an index, called Gaussian fuzzy index (GFI), recently developed by the authors, based on the notion of fuzzy set theory, for validating the clusters obtained by a clustering algorithm applied on cancer gene expression data. GFI is then used for the identification of genes that have altered quite significantly from normal state to carcinogenic state with respect to their mRNA expression patterns. The effectiveness of the methodology has been demonstrated on three gene expression cancer datasets dealing with human lung, colon and leukemia. The performance of GFI is compared with 19 exiting cluster validity indices. The results are appropriately validated biologically and statistically. In this context, we have used biochemical pathways, p-value statistics of GO attributes, t-test and zscore for the validation of the results. It has been reported that GFI is capable of identifying high-quality enriched clusters of genes, and thereby is able to select more cancer-mediating genes.

Classification of the severity of diabetic neuropathy: a new approach taking uncertainties into account using fuzzy logic

OBJECTIVE: This study proposes a new approach that considers uncertainty in predicting and quantifying the presence and severity of diabetic peripheral neuropathy. METHODS: A rule-based fuzzy expert system was designed by four experts in diabetic neuropathy. The model variables were used to classify neuropathy in diabetic patients, defining it as mild, moderate, or severe. System performance was evaluated by means of the Kappa agreement measure, comparing the results of the model with those generated by the experts in an assessment of 50 patients. Accuracy was evaluated by an ROC curve analysis obtained based on 50 other cases; the results of those clinical assessments were considered to be the gold standard. RESULTS: According to the Kappa analysis, the model was in moderate agreement with expert opinions. The ROC analysis (evaluation of accuracy) determined an area under the curve equal to 0.91, demonstrating very good consistency in classifying patients with diabetic neuropathy. CONCLUSION: The model efficiently classified diabetic patients with different degrees of neuropathy severity. In addition, the model provides a way to quantify diabetic neuropathy severity and allows a more accurate patient condition assessment.

A fuzzy system for cloacal temperature prediction of broiler chickens / Sistema fuzzy para a predição da temperatura cloacal de frangos de corte

Cloacal temperature (CT) of broiler chickens is an important parameter to classify its comfort status; therefore its prediction can be used as decision support to turn on acclimatization systems. The aim of this research was to develop and validate a system using the fuzzy set theory for CT prediction of broiler chickens. The fuzzy system was developed based on three input variables: air temperature (T), relative humidity (RH) and air velocity (V). The output variable was the CT. The fuzzy inference system was performed via Mamdani's method which consisted in 48 rules. The defuzzification was done using center of gravity method. The fuzzy system was developed using MAPLE® 8. Experimental results, used for validation, showed that the average standard deviation between simulated and measured values of CT was 0.13°C. The proposed fuzzy system was found to satisfactorily predict CT based on climatic variables. Thus, it could be used as a decision support system on broiler chicken growth.

A temperatura cloacal (TC) de frangos de corte é um importante parâmetro para classificar a sua condição de conforto, portanto, a sua predição pode ser usada no suporte à decisão de acionamento de sistemas de climatização. Objetivou-se com a presente pesquisa desenvolver e validar um sistema, utilizando a teoria dos conjuntos fuzzy para predição da TC de frangos de corte. O sistema fuzzy foi desenvolvido com base em três variáveis de entrada: temperatura do ar (T), umidade relativa (UR) e velocidade do ar (V), tendo, como variável de saída, a TC. A inferência fuzzy foi realizada por meio do método tipo Mamdani, que consistiu na elaboração de 48 regras e a defuzzificação por meio do método do Centro de Gravidade. O sistema fuzzy foi desenvolvido no ambiente computacional MAPLE® 8. Resultados experimentais, usados para a validação, mostraram que o desvio padrão médio entre os valores simulados e medidos de TC foi de 0,13°C. O sistema fuzzy proposto prediz satisfatoriamente a TC com base nas variáveis climáticas, podendo ser utilizado como suporte à decisão em sistemas de criação de frangos de corte.

Comparación de la percepción del peso de una carga en población laboral femenina: sector industrial versus sector salud / Comparison of the perceptions of load heaviness by female working population: industry sector versus health sector

La evidencia disponible indica que existe fuerte asociación entre dolor lumbar y manejo manual de carga. Atendiendo a la naturaleza de la carga manipulada, se pueden distinguir dos grandes grupos de trabajadoras: las que realizan manejo de materiales y aquellas que realizan fundamentalmente manejo de pacientes. En ambos sectores se han identificado factores de riesgo de naturaleza física, organizacional y psicosocial que estarían asociados a ese diagnóstico, pero a pesar de los esfuerzos en prevención, este problema sigue siendo importante. La evidencia sugiere que el problema sería más crítico en el sector de la salud. Los autores de la presente investigación proponen que es necesario ocupar herramientas que pertenecen al ámbito de la Ingeniería Cognitiva para explorar las causas de esta tendencia. Esta investigación fue llevada a cabo en una muestra de trabajadoras que pertenecen al sector industrial y al sector de la salud con el objetivo de determinar si existe diferencia significativa asociada a distintas categorías de percepción de la carga manipulada (ej., moderada, pesada,máxima, etc.). Los resultados demuestran que existe diferencia en la percepción del esfuerzo entre ambos grupos de trabajadoras. Esencialmente, esto implica que ambas poblaciones deberían ser estudiadas en forma separada y, en particular, las iniciativas de prevención de los trastornos músculoesqueléticos vinculadas al manejo manual de carga deberían incorporar también elementos propios del proceso cognitivo que gobierna la percepción de la carga de trabajo. Esto sería especialmente importante en las investigaciones desarrolladas con el propósito de establecer límites de peso seguros para la población laboral femenina nacional.

The available evidence indicates that there is a strong association between low back pain and manual load handling. According to the type of load, two large groups of workers can be distinguished: those handling materials and those handling mainly patients. Among bothof these groups, physical, organizational and psychosocial risk factors, probably related to this diagnosis, have been identified. In spite of efforts to prevent it, this situation remains relevant. Evidence suggests that this problem is more critical in the health sector. Authors of the present study propose the use of tools pertaining to Cognitive Engineering and thus to explore the causes of this tendency. This research was carried out in a sample of workers from the industry and health sectors. The study’s goal was to determine ifthere is a significant difference associated to different perception categories of load heaviness (e.g. moderate, heavy, very heavy, etc.). Results show a difference in effort perception between groups. Essentially, this implies that both populations should be studied separately and initiatives intended to prevent musculoskeletaldisorder related to load handling should also include elements which are characteristic of the cognitive process controlling the perception of load heaviness. All of this would be especially important in studies conducted with the purpose of establishing safe weight limits for the nation’s female workers.

Identificabilidade e estabilidade dos parâmetros no método Grade of Membership (GoM): considerações metodológicas e práticas / Identifiability and stability of standards in the Grade of Membership (GoM) method: methodological and practical considerations / Identificabilidad y estabilidad de los parámetros en el método Grade of Membership (GoM): consideraciones metodológicas y prácticas

O método Grade of Membership (GoM) tem sido cada vez mais utilizado por demógrafos brasileiros e tem a vantagem de possuir um parâmetro que mensura a heterogeneidade individual, com base nas correlações não-observáveis entre as categorias de resposta das variáveis de interesse, gerando um medida do grau de pertencimento de cada indivíduo a perfis extremos. Alguns autores, contudo, chamam atenção para questões importantes na calibragem dos modelos finais que utilizam o programa GoM versão 3.4, como o problema de identificabilidade - soluções múltiplas para parâmetros estimados. Neste artigo, é sugerido um procedimento capaz de identificar um modelo final com solução única que descreva os tipos puros mais fidedignos à base de dados, em uma tentativa de otimização. Para ilustrar esse processo, utilizou-se uma base de dados correspondente a um levantamento econômico e sociodemográfico de uma população de pequenos agricultores residentes ao longo da Rodovia Transamazônica, no Estado do Pará. Também identificou-se a existência de instabilidade nos parâmetros estimados pelo programa GoM 3.4, sendo proposto um método de estabilização de seus valores. Com esses procedimentos combinados, os usuários do programa GoM 3.4 poderão descrever sua base de dados de forma mais adequada e responder às críticas sobre questões de identificabilidade e estabilidade dos modelos resultantes. Essas soluções empíricas são relevantes por afetarem cálculos de prevalência e de incidência de eventos de interesse, além de trazerem consequências importantes sobre o ponto e o momento corretos para intervenções de políticas públicas ou de planejamento prospectivo em análises de projeção.

The Grade of Membership (GoM) method has been increasingly employed by Brazilian demographers, and has the advantage of including a parameter that measures individual heterogeneousness on the basis of non-observable correlations among the categories of responses to variables of interest. The parameter shows each individual's degree of membership to extreme profiles. Several authors, however, have called attention to important issues in adjusting the final models that use 3.4 Version of the GoM Program, such as the problem of identifiability - multiple solutions for estimated parameters. In this article a procedure is discussed that is able to identify a final model with a single solution that describes the pure types that are the most reliable for the database, in an attempt at streamlining. To illustrate this process, a database was used with data corresponding to an economic and sociodemographic study of a population of small farmers living along the TransAmazon Highway, in the northern State of Pará, Brazil. The existence of instability in the parameters estimated by the GoM 3.4 Program was also identified and a method of stabilization of its values was proposed. With these combined procedures, users of the GoM 3.4 Program will be able to describe their databases more adequately and respond to criticisms regarding the identifiability and stability of the resulting models. These empirical solutions are significant. Not only do they affect calculations of prevalence and incidence of events of interest, they also bring about important consequences at the correct point and correct moment for interventions of public policies or of prospective planning in projection analyses.

El método Grade of Membership (GoM) ha sido cada vez más utilizado por los demógrafos brasileños y tiene la ventaja de poseer un parámetro que mide la heterogeneidad individual, sobre la base de las correlaciones no observables entre las categorías de respuesta de las variables de interés, generando una medida del grado de pertenencia de cada individuo a perfiles extremos. Algunos autores, sin embargo, destacan cuestiones importantes en la calibración de los modelos finales que utiliza el programa GoM versión 3.4, como el problema de identificabilidad - soluciones múltiples para parámetros estimados. En este artículo, se sugiere un procedimiento capaz de identificar un modelo final con una solución única que describa los tipos puros de mayor fidelidad con respecto a la base de datos, con una intención de optimización. Para ilustrar este proceso, se utilizó una base de dados correspondiente a un relevamiento económico y socio-demográfico de una población de pequeños agricultores residentes a lo largo de la Autopista Transamazônica, en el Estado de Pará. También se identificó la existencia de inestabilidad en los parámetros estimados por el programa GoM 3.4, y se propuso un método de estabilización de sus valores. Con esos procedimientos combinados, los usuarios del programa GoM 3.4 podrán describir su base de dados en forma más adecuada y responder a las críticas sobre cuestiones de identificabilidad y estabilidad de los modelos resultantes. Estas soluciones empíricas son relevantes porque afectan cálculos de superioridad y de incidencia de eventos de interés, además de traer consecuencias importantes sobre el punto y el momento correctos para las intervenciones de políticas públicas o de planificación prospectiva en análisis de proyección.

A metodologia ROC na avaliação de um modelo fuzzy de predição do estádio patológico do tumor de próstata / The ROC methodology in the evaluation of a fuzzy model to prediction of the prostate tumor's pathological stage

Nos últimos anos o aumento da incidência de casos de câncer de próstata configura-se como um importante problema de saúde pública e um desafio para a ciência médica. O objetivo deste trabalho é a avaliação do desempenho de um modelo matemático, desenvolvido por Silveira (2007) para predizer o estadiamento patológico do câncer de próstata, por meio da metodologia ROC (Receiver Operating Characteristic). O modelo consiste num sistema baseado em regras fuzzy (SBRF), que combina os dados pré-cirúrgicos – estado clínico, nível de PSA e grau de Gleason – acionando um conjunto de regras linguísticas, elaboradas com base nas informações presentes nos nomogramas já existentes. A saída do sistema fornece as possibilidades do indivíduo, com determinado quadro clínico, se enquadrar em cada um dos estádios de extensão do tumor: localizado, localmente avançado e metastático. Para a análise do poder discriminatório do modelo fuzzy como um teste de diagnóstico, foi construída, a partir das medidas de sensibilidade e especificidade, a curva ROC e calculada a área total sob a curva, como medida de desempenho. Além disso, foram obtidos (de duas maneiras distintas) os pontos de corte mais “adequados”, isto é, um limiar de decisão entre a doença estar totalmente localizada no interior da glândula prostática ou não. Dados reais de pacientes do Hospital de Clínicas da UNICAMP foram usados nos cálculos e a cirurgia– prostatectomia radical – foi adotada como padrão-ouro. Os resultados alcançados mostraram que o modelo fuzzy em questão pode vir a ser utilizado para discriminar câncer de próstata localizado.

In recent years, the increase in the incidence of prostate cancer has become a major public health problem and a challenge for medical science. The goal of this work is assessing the performance of a mathematical model, developed by Silveira (2007) to predict the pathological stage of the prostate cancer, through ROC methodology (Receiver Operating Characteristic). The model is a fuzzy rule based system, that combines pre-surgical data – clinical stage, PSA level and Gleason score – availing of a set of linguistic rules made with base on information of the existents nomograms. The output of the system provides the possibilities of the individual, with certain clinical features, be in each stage of the tumor extension: localized, advanced locally and metastatic. To analyze the discriminatory power of the fuzzy model as a diagnosis test, was constructed from the measures of sensitivity and specificity, the ROC curve and calculated the total area under the curve, as measure of performance. Moreover, were obtained (in two different ways) the cutoff points most “appropriate”, that is a threshold for deciding between the disease is fully localized within the prostate gland or not. Real data of patients from the Clinics Hospital of UNICAMP were used in the calculations and the surgery – radical prostatectomy – was used as gold standard. The results showed that the fuzzy model in question can be used to discriminate localized prostate cancer.

Modelo híbrido de aprendizagem utilizando expressões linguísticas e numéricas no auxílio ao diagnóstico / Hybrid model of learning using linguistic expressions and numerical in the diagnosis

O objetivo desta pesquisa é a análise de modelos de aprendizagem, utilizando diferentes operações aritméticas aplicadas de Sistemas Neuro-Fuzzy (NFS)...