Modelo predictivo de enfermedad cardiovascular basado en inteligencia artificial en la atención primaria de salud / A Predictive Model for Cardiovascular Disease based on Artificial Intelligence in Primary Health Care

Introducción: En Cuba y en el resto del mundo, las enfermedades cardiovasculares son reconocidas como un problema de salud pública mayúsculo y creciente, que provoca una alta mortalidad. Objetivo: Diseñar un modelo predictivo para estimar el riesgo de enfermedad cardiovascular basado en técnicas de inteligencia artificial. Métodos: La fuente de datos fue una cohorte prospectiva que incluyó 1633 pacientes, seguidos durante 10 años, fue utilizada la herramienta de minería de datos Weka, se emplearon técnicas de selección de atributos para obtener un subconjunto más reducido de variables significativas, para generar los modelos fueron aplicados: el algoritmo de reglas JRip y el meta algoritmo Attribute Selected Classifier, usando como clasificadores el J48 y el Multilayer Perceptron. Se compararon los modelos obtenidos y se aplicaron las métricas más usadas para clases desbalanceadas. Resultados: El atributo más significativo fue el antecedente de hipertensión arterial, seguido por el colesterol de lipoproteínas de alta densidad y de baja densidad, la proteína c reactiva de alta sensibilidad y la tensión arterial sistólica, de estos atributos se derivaron todas las reglas de predicción, los algoritmos fueron efectivos para generar el modelo, el mejor desempeño fue con el Multilayer Perceptron, con una tasa de verdaderos positivos del 95,2 por ciento un área bajo la curva ROC de 0,987 en la validación cruzada. Conclusiones: Fue diseñado un modelo predictivo mediante técnicas de inteligencia artificial, lo que constituye un valioso recurso orientado a la prevención de las enfermedades cardiovasculares en la atención primaria de salud(AU)

Introduction: In Cuba and in the rest of the world, cardiovascular diseases are recognized as a major and growing public health problem, which causes high mortality. Objective: To design a predictive model to estimate the risk of cardiovascular disease based on artificial intelligence techniques. Methods: The data source was a prospective cohort including 1633 patients, followed for 10 years. The data mining tool Weka was used and attribute selection techniques were employed to obtain a smaller subset of significant variables. To generate the models, the rule algorithm JRip and the meta-algorithm Attribute Selected Classifier were applied, using J48 and Multilayer Perceptron as classifiers. The obtained models were compared and the most used metrics for unbalanced classes were applied. Results: The most significant attribute was history of arterial hypertension, followed by high and low density lipoprotein cholesterol, high sensitivity c-reactive protein and systolic blood pressure; all the prediction rules were derived from these attributes. The algorithms were effective to generate the model. The best performance was obtained using the Multilayer Perceptron, with a true positive rate of 95.2percent and an area under the ROC curve of 0.987 in the cross validation. Conclusions: A predictive model was designed using artificial intelligence techniques; it is a valuable resource oriented to the prevention of cardiovascular diseases in primary health care(AU)

Data mining in traditional Chinese medicine product quality review / 中国中药杂志

The traditional Chinese medicine(TCM) enterprises have accumulated a large amount of product quality review(PQR) data. Mining these data can reveal the hidden knowledge in production and helps improve pharmaceutical manufacturing technology. However, there are few studies involving the mining of PQR data and thus enterprises lack the guidance to analyze the data. This study proposed a method to mine the PQR data, which consisted of 4 functional modules: data collection and preprocessing, risk classification of variables, risk evaluation by batches, and the regression analysis of quality. Further, we carried out a case study of the formulation process of a TCM product to illustrate the method. In the case study, the data of 398 batches of products during 2019-2021 were collected, which contained 65 process variables. The risks of variables were classified according to the process performance index. The risk of each batch was analyzed through short-term and long-term evaluation, and the critical variables with the strongest impact on the product quality were identified by partial least square regression. The results showed that 1 variable and 13 batches were of high risk, and the critical process variable was the quality of the intermediates. The proposed method enables enterprises to comprehensively mine the PQR data and helps to enhance the process understanding and improve the quality control.

Obtención automática de palabras clave en textos clínicos: una aplicación de procesamiento del lenguaje natural a datos masivos de sospecha diagnóstica en Chile / Automatic keyword retrieval from clinical texts: an application of natural language processing to massive data of Chilean suspected diagnosis

Background: Free-text imposes a challenge in health data analysis since the lack of structure makes the extraction and integration of information difficult, particularly in the case of massive data. An appropriate machine-interpretation of electronic health records in Chile can unleash knowledge contained in large volumes of clinical texts, expanding clinical management and national research capabilities. Aim: To illustrate the use of a weighted frequency algorithm to find keywords. This finding was carried out in the diagnostic suspicion field of the Chilean specialty consultation waiting list, for diseases not covered by the Chilean Explicit Health Guarantees plan. Material and Methods: The waiting lists for a first specialty consultation for the period 2008-2018 were obtained from 17 out of 29 Chilean health services, and total of 2,592,925 diagnostic suspicions were identified. A natural language processing technique called Term Frequency-Inverse Document Frequency was used for the retrieval of diagnostic suspicion keywords. Results: For each specialty, four key words with the highest weighted frequency were determined. Word clouds showing words weighted by their importance were created to obtain a visual representation. These are available at cimt.uchile.cl/lechile/. Conclusions: The algorithm allowed to summarize unstructured clinical free-text data, improving its usefulness and accessibility.

Design of a geospatial model applied to Health management / La producción de un modelo geoespacial aplicado a la gestión de la salud / Concepção de um modelo geoespacial aplicado na gestão à saúde

ABSTRACT Objective: To identify geographically the beneficiaries categorized as prone to Type 2 Diabetes Mellitus, using the recognition of patterns in a database of a health plan operator, through data mining. Method: The following steps were developed: the initial step, the information survey. Development, construction of the process of extraction, transformation, and loading of the database. Deployment, presentation of the geographical information through a georeferencing tool. Results: As a result, the mapping of Paraná according to its health care network and the concentration of Type 2 Diabetes Mellitus is presented, enabling the identification of cause-and-effect relationships. Conclusion: It is concluded that the analysis of georeferenced information, linked to health information obtained through the data mining technique, can be an excellent tool for the health management of a health plan operator, contributing to the decision-making process in Health.

RESUMEN Objetivo: Identificar geográficamente a los beneficiarios categorizados como propensos a la enfermedad Diabetes mellitus tipo 2, utilizándose el reconocimiento de patrones en una base de datos de cierta compañía de seguro médico por medio de la minería de datos. Método: Se desarrollaron las siguientes etapas: fase inicial, levantamiento de información. Desarrollo, construcción del proceso de extracción, transformación y carga en la base de datos. Implantación, presentación de la información geográfica mediante la herramienta de georreferenciación. Resultados: Se presenta el mapeo de Paraná (Brasil) con relación a su red asistencial y la concentración de Diabetes mellitus tipo 2, proporcionando la identificación de las relaciones de causa-efecto. Conclusión: Se concluyó que el análisis de las informaciones georreferenciadas, vinculadas a las informaciones de salud obtenidas por la técnica de minería de datos, puede ser una excelente herramienta en la gestión de salud de cierta compañía de seguro médico, lo que contribuye al apoyo a la toma de decisiones en salud.

RESUMO Objetivo: Identificar geograficamente os beneficiários categorizados como propensos à doença Diabetes Mellitus Tipo 2, utilizando o reconhecimento de padrões em uma base de dados de uma operadora de plano de saúde, por meio da mineração de dados. Método: Desenvolveram-se as seguintes etapas: fase inicial, levantamento de informações. Desenvolvimento, construção do processo de extração, transformação e carga do banco de dados. Implantação, apresentação das informações geográficas por meio da ferramenta de georreferenciamento. Resultados: Como resultados, apresenta-se o mapeamento do Paraná em relação a sua rede assistencial e a concentração de Diabetes Mellitus Tipo 2, oportunizando a identificação de relações de causa-efeito. Conclusão: Conclui-se que a análise de informações georreferenciadas, vinculadas às informações de saúde obtidas por meio da técnica de mineração de dados, pode ser um excelente instrumento para a gestão da saúde de uma operadora de plano de saúde, contribuindo para o apoio à tomada de decisões em saúde.

La analítica del aprendizaje en educación médica: ¿punto de inflexión? / Learning analytics in medical education: turning point?

Resumen La analítica del aprendizaje es una disciplina novedosa que tiene un enorme potencial para mejorar la calidad de la educación médica y la evaluación del aprendizaje. Se define como: “la medición, recopilación, análisis y reporte de datos sobre los alumnos y sus contextos, con el propósito de entender y optimizar el aprendizaje y los entornos en que ocurre”. En las últimas décadas, la aparición de grandes volúmenes de datos (big data), acompañada de una rápida evolución en la minería de datos educativos, la aparición de tecnologías sofisticadas para analizar y visualizar datos de cualquier tipo, así como la disponibilidad de dispositivos móviles con conectividad permanente, mayor velocidad de procesamiento y capacidad de recuperación de información, han generado un contexto que favorece el uso de la analítica del aprendizaje en la medicina clínica y la educación médica. En este artículo se describe la historia reciente del concepto de analítica del aprendizaje, sus ventajas y desventajas en educación superior, así como sus aplicaciones en la enseñanza de las ciencias de la salud y la evaluación educativa. Es necesario que la comunidad de educadores médicos conozca la analítica del aprendizaje, para ser capaces de integrarla en su contexto eficaz y oportunamente.

Abstract Learning analytics is an innovative discipline that has an enormous potential to improve the quality of medical education and learning assessment. It is defined as: “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs”. In recent decades, the appearance of large volumes of data (big data), accompanied by a quick evolution of educational data mining techniques, the emergence of sophisticated technologies to analyze and visualize any type of data, as well as the availability of permanently-connected mobile electronic devices, higher processing speed and capacity of information retrieval, have generated a context that favors the use of learning analytics in clinical medicine and medical education. In this paper, the recent history of the concept of learning analytics is described, as well as its advantages and disadvantages in higher education, and its applications in the teaching of health sciences and educational assessment. It is necessary for the community of medical educators to be acquainted with learning analytics, in order to be able to integrate it to our context in an efficacious and timely manner.

O uso do Twitter como minerador de eventos adversos de medicamentos de combate à malária: o caso da doxiciclina / El uso de Twitter como localizador de eventos adversos con medicamentos de combate a la malaria: el caso de la doxiciclina / Mining in Twitter for adverse events from malaria drugs: the case of doxycycline

Durante o período de pós-comercialização, quando medicamentos são usados por grandes populações e por períodos de tempo maiores, eventos adversos (EA) inesperados podem ocorrer, o que pode alterar a relação risco-benefício dos medicamentos o suficiente para exigir uma ação regulatória. Eventos adversos são agravos à saúde que podem surgir durante o tratamento com um produto farmacêutico, os quais, no período de pós-comercialização do medicamento, podem requerer um aumento significativo de cuidados de saúde e resultar em danos desnecessários aos pacientes, muitas vezes fatais. Portanto, o quanto antes, a descoberta de EA no período de pós-comercialização é um objetivo principal do sistema de saúde. Alguns países possuem sistemas de vigilância farmacológica responsáveis pela coleta de relatórios voluntários de EA na pós-comercialização, mas estudos já demonstraram que, com a utilização de redes sociais, pode-se conseguir um número maior e mais rápido de relatórios. O objetivo principal deste projeto é construir um sistema totalmente automatizado que utilize o Twitter como fonte para encontrar EA novos e já conhecidos e fazer a análise estatística dos dados obtidos. Para isso, foi construído um sistema que coleta, processa, analisa e avalia tweets em busca de EA, comparando-os com dados da Agência Americana de Controle de Alimentos e Medicamentos (FDA) e do padrão de referência construído. Nos resultados obtidos, conseguimos encontrar EA novos e já existentes relacionados ao medicamento doxiciclina, o que demonstra que o Twitter, quando utilizado em conjunto com outras fontes de dados, pode ser útil para a farmacovigilância.

Durante el período de poscomercialización, cuando grandes poblaciones consumen medicamentos durante períodos más prolongados de tiempo, se pueden producir eventos adversos (EA) inesperados, lo que puede alterar la relación riesgo-beneficio de los medicamentos. Esta situación es suficiente para exigir una acción regulatoria. Los EA son agravios a la salud que pueden surgir durante el tratamiento con un producto farmacéutico, los cuales, durante el período de poscomercialización del medicamento, pueden requerir un aumento significativo de cuidados de salud y resultar en lesiones innecesarias para los pacientes, muchas veces fatales. Por lo tanto, el hallazgo anticipado de EA durante el período de poscomercialización es un objetivo primordial del sistema de salud. Algunos países cuentan con sistemas de vigilancia farmacológica, responsables de la recogida de informes voluntarios de EA durante la poscomercialización, pero algunos estudios ya demostraron que, con la utilización de las redes sociales, se puede conseguir un número de informes mayor y más rápido. El objetivo principal de este proyecto es construir un sistema totalmente automatizado que utilice Twitter como fuente para encontrar nuevos EA y ya conocidos, además de realizar un análisis estadístico de los datos obtenidos. Para tal fin, se construyó un sistema que recoge, procesa, analiza y evalúa tweets en búsqueda de eventos adversos, comparándolos con datos de la Agencia Americana de Control de Alimentos y Medicamentos (FDA) y del estándar de referencia construido. En los resultados obtenidos, conseguimos encontrar nuevos eventos adversos y ya existentes, relacionados con el medicamento doxiciclina, lo que demuestra que Twitter, cuando es utilizado junto a otras fuentes de datos, puede ser útil para la farmacovigilancia.

During the post-marketing period, when medicines are used by large population contingents and for longer periods, unexpected adverse events (AE) can occur, potentially altering the drug's risk-benefit ratio enough to demand regulatory action. AE are health problems that can occur during treatment with a pharmaceutical product, which in the drug's post-marketing period can require a significant increase in health care and result in unnecessary and often fatal harm to patients. Therefore, a key objective for the health system is to identify AE as soon as possible in the post-marketing period. Some countries have pharmacovigilance systems responsible for collecting voluntary reports of post-marketing AE, but studies have shown that social networks can be used to obtain more and faster reports. The current project's main objective is to build a totally automated system using Twitter as a source to detect both new and previously known AE and conduct the statistical analysis of the resulting data. A system was thus built to collect, process, analyze, and assess tweets in search of AE, comparing them to U.S. Food and Drug Administration (FDA) data and the reference standard. The results allowed detecting new and existing AE related to the drug doxycycline, showing that Twitter can be useful in pharmacovigilance when employed jointly with other data sources.

MIDAS: Aplicación informática para la identificación de microsatélites exactos e inexactos en secuencias genómicas / MIDAS: Computer application for the identification of exact and inaccurate microsatellites in genomic sequences

Los microsatélites son secuencias cortas repetidas en tándem, frecuentes y diversas en los genomas de todas las especies, constituyendo importantes marcadores en múltiples áreas de investigación basadas en la genómica. Se han encontrado asociaciones de estos marcadores a un número importante de enfermedades en humanos. En el desarrollo de vacunas se ha demostrado cómo los patógenos pueden evadir la respuesta inmune simplemente alterando la composición de las secuencias repetidas en sus genes. Existen numerosas aplicaciones informáticas destinadas a la detección de estas secuencias, no obstante, éstas no cubren todas las expectativas debido a la divergencia de criterios y enfoques aplicados a la solución del problema de su detección. MIDAS implementa una solución no heurística basada en dos algoritmos combinatorios en serie: el primero detecta microsatélites exactos, y el segundo, de permitirlo los parámetros del modelo, extiende las secuencias a su versión inexacta óptima. La aplicación tiene como entrada la secuencia genómica en formato GBFF o FASTA y su salida brinda las posiciones de los microsatélites en la secuencia genómica, así como tamaños, alineamientos, flancos, posiciones, etc. El algoritmo tiene una elevada eficiencia y es exhaustivo, detectando todas las posibles secuencias repetidas independientemente de su composición nucleotídica(AU)

Microsatellites are tandem repeat, frequent and diverse short sequences in the genomes of all species, constituting important markers in multiple areas of genomics-based research. Associations of these markers have been found in a significant number of human diseases. Vaccine development has shown how pathogens can evade the immune response by simply altering the composition of repeat sequences in their genes. There are numerous computer applications for the detection of these sequences, but they do not meet all expectations due to the divergence of criteria and approaches applied to solving the problem of their detection. MIDAS implements a non-heuristic solution based on two combinatorial algorithms in series: the first one detects exact microsatellites, and the second one, if the model parameters allow it, extends the sequences to their optimal inaccurate version. The application has as input the genomic sequence in GBFF or FASTA format and its output provides the microsatellite positions in the genomic sequence, as well as sizes, alignments, flanks and other statistics. The algorithm is highly efficient and comprehensive, detecting all possible repeat sequences regardless of their nucleotide composition(AU)

Integración de técnicas de Minería de procesos para la detección de la variabilidad en procesos hospitalarios desde sistemas automatizados / Integrating mining processes techniques in hospital procedures for the detection of variability from their information systems

Los sistemas de información hospitalaria cuentan con un volumen importante de datos, sin embargo, carecen de mecanismos que permitan analizar la ejecución de los procesos e identificar variabilidad. La variabilidad puede observarse en prácticamente cada paso del proceso asistencial y a varios niveles de agrupación: poblacional e individual. Desde el punto de vista poblacional se comparan tasas de realización de un procedimiento clínico, como pueden ser intervenciones quirúrgicas o ingresos hospitalarios en un período de tiempo. Las técnicas de minería de procesos analizan los datos reales de sistemas informáticos y son útiles para la detección de variabilidad en la ejecución de los procesos de negocio. La presente investigación propone la aplicación de técnicas de minería de procesos, seleccionadas a partir de un riguroso estudio del estado del arte, para el análisis de los procesos hospitalarios desde sus sistemas de información y materializadas en un modelo computacional. El Modelo para la Detección de Variabilidad (MDV) se instrumentó exitosamente en el sistema XAVIA HIS desarrollado por la Universidad de las Ciencias Informáticas UCI, donde fueron adaptadas e integradas las técnicas de minería de procesos. El modelo MDV contribuye al proceso de informatización de la salud en Cuba. La solución propicia la utilización de una tecnología emergente en áreas como la industrial y empresarial en el entorno sanitario. Esta beneficia importantes funciones gerenciales como la gestión, control y planificación de recursos y servicios sanitarios(AU)

The hospital information systems collect an important volume of data, however, they lack mechanisms to analyze the execution of the processes and identify variability. In practically every step of the care process and at various levels of grouping: population and individual the variability is present. From a population point of view, performance rates of a clinical procedure such as surgical interventions or hospital admissions, are compared over time. Process mining techniques analyze the real data of computer systems and are useful for the detection of variability in the execution of business processes. Based on a rigorous study of the state of the art, this research proposes the application of process mining techniques for the analysis of hospital processes from their information systems, providing a computational model. Model for Variability Detection (MDV) implemented successfully in the XAVIA HIS system developed by the UCI University of Informatics Sciences, where techniques of process mining were adapted and integrated. The MDV model contributes to the process of computerization of health in Cuba. The solution encourages the use of an emerging technology in areas such as industrial and business in the healthcare environment. This benefits important management functions such as control and planning of resources and health services(AU)

Analysis of influencing factors of severity in acute pancreatitis using big data mining

SUMMARY OBJECTIVES To evaluate the epidemiological characteristics of acute pancreatitis (AP) and explore potential relationships between these factors and severity. METHODOLOGY Data-sets of 5,659 patients with AP from health statistics and the Information Center of Jiangsu province, between 2014 and 2016, were analyzed. A self-organizing map (SOM) neural network was used for data clustering. RESULTS Biliary acute pancreatitis (BAP) (86.7%) was the most frequent etiological factor. A total of 804 (14.2%) patients had severe acute pancreatitis (SAP). The mean age of patients was 53.7 + 17.3 (range 12~94y). Most of the AP patients were married (75.4%); 6% of mild /moderately severe AP (MAP/MASP) patients were unmarried, which was less than SAP patients (P=0.016). AP patients with blood type AB in the general population (8.8%) was significantly lower than that of AP cases (13.9%) (P=0.019) and SAP cases(18.7%) (P=0.007). The number of AP patients in southern Jiangsu was much higher than that in northern Jiangsu province, especially in Nanjing (1229, 21.7%). The proportion of acute alcoholic pancreatitis (AAP) in the north of Jiangsu (Xuzhou 18.4%) was much higher than that in southern Jiangsu (Suzhou 2.6%). The whole sample was divided into five classes by SOM neural network. If BAP patients were male, old, divorced, and blood type AB or B, they were more likely to develop SAP. Middle-age, unmarried or divorced male patients with blood type B/AB who suffered from HAP or AAP were also more likely to develop SAP. CONCLUSIONS The number of unmarried patients with MAP/MASP was smaller than that of SAP. Blood types AB and B were more frequent in AP, especially in SAP. The differences between southern Jiangsu and northern Jiangsu, in number of AP patients and the proportion of AAP, were significant. In class I and class IV, the ratio of SAP was much higher than in other classes and the whole sample.

Mineração de dados na avaliação de óbitos após cirurgia de amputação / Data mining to evaluate mortality after amputation surgery

Contexto: A amputação e a desarticulação objetivam melhorar a saúde de um indivíduo, mas esses tratamentos apresentam taxas significantes de mortalidade que variam de acordo com os fatores relacionados. Objetivo: Identificar as associações entre os determinantes da mortalidade pós-operatória da amputação. Métodos: Estudo do tipo caso-controle (óbito versus não óbito) em que foi adotada a descoberta de regras de associação (abordagem da mineração de dados) e métricas epidemiológicas sobre 173 registros de pacientes amputados em um hospital público de Santa Catarina em 2014. Resultados: Os principais determinantes foram: idade > 60 anos [ odds ratio (OR) = 3,0], sexo feminino (OR = 2,0), baixa escolaridade, hipertensão (OR = 3,0), diabetes (OR = 1,6) e tabagismo (OR = 1,8). Dos pacientes com idade entre 60 a 69 anos (38%), 87,9% evoluíram para alta, estando o óbito associado a doença vascular periférica. Quando a idade foi > 70 anos, embolia e trombose de artérias dos membros inferiores foram o fator de exceção (óbito). As patologias com maior associação ao óbito foram doença vascular (47,0%), diabetes (29,4%), doença cardíaca (razão de risco = 11,4), doença renal (OR = 10,4) e doença pulmonar (OR = 5,2). As cirurgias proximais estiveram mais associadas ao óbito do que as distais. Entre os pacientes que foram a óbito, 76,0% foram submetidos a raquianestesia e 24,0% a anestesia geral. Conclusão: A mineração de dados permitiu identificar as associações vinculadas ao óbito entre as diferentes variáveis e diagnósticos, como por exemplo, entre idade > 70 anos e diagnóstico de embolia e trombose de artérias dos membros inferiores

Background: The objective of amputation and disarticulation is to improve health. However, these treatments are associated with significant mortality rates that vary in relation to risk factors. Objective: To identify associations between determinants of postoperative mortality after amputation surgery. Methods: Case-control study (death vs. no death) considering data from 173 patients who underwent amputation surgery at a public hospital in Santa Catarina state, Brazil. These data were analyzed using a data mining approach to discover association rules and epidemiologic association metrics. Results: The main determinants were age > 60 years (odds ratio (OR) = 3.0), female sex (OR = 2.0), low education, hypertension (OR = 3.0), diabetes (OR = 1.6), and smoking (OR = 1.8). Among patients aged 60-69 years, 87.9% survived to discharge from hospital. The exceptions occurred when patients in this age range had peripheral vascular disease. The same was true when age was > 70 years, among whom diagnoses of embolism and thrombosis of arteries of the lower extremities were the exception factors (associated with death). The most common pathologies associated with death were vascular disease (47.0%) and diabetes (29.4%), heart disease (relative risk = 11.4), renal disease (OR = 10.4), and lung disease (OR = 5.2). Proximal surgeries were more strongly associated with death than distal ones. Among the deaths, 76.0% had been given spinal anesthesia and 24.0% general anesthesia. Conclusion: Data mining enabled identification of associations between death and a variety of different variables and diagnostic hypotheses; for example, age > 70 years and diagnosis of embolism and thrombosis of arteries of the lower extremities

Application of data mining techniques to explore predictors of upper urinary tract damage in patients with neurogenic bladder

This study proposed a decision tree model to screen upper urinary tract damage (UUTD) for patients with neurogenic bladder (NGB). Thirty-four NGB patients with UUTD were recruited in the case group, while 78 without UUTD were included in the control group. A decision tree method, classification and regression tree (CART), was then applied to develop the model in which UUTD was used as a dependent variable and history of urinary tract infections, bladder management, conservative treatment, and urodynamic findings were used as independent variables. The urethra function factor was found to be the primary screening information of patients and treated as the root node of the tree; Pabd max (maximum abdominal pressure, >14 cmH2O), Pves max (maximum intravesical pressure, ≤89 cmH2O), and gender (female) were also variables associated with UUTD. The accuracy of the proposed model was 84.8%, and the area under curve was 0.901 (95%CI=0.844-0.958), suggesting that the decision tree model might provide a new and convenient way to screen UUTD for NGB patients in both undeveloped and developing areas.

Calibration of the maximum carboxylation velocity (Vcmax) using data mining techniques and ecophysiological data from the Brazilian semiarid region, for use in Dynamic Global Vegetation Models / Calibração da velocidade máxima de carboxilação (Vcmax), utilizando técnicas de mineração de dados e dados de ecofisiologia da região semiárida brasileira, para uso em Modelos de Vegetação Globais Dinâmicos

Abstract The semiarid region of northeastern Brazil, the Caatinga, is extremely important due to its biodiversity and endemism. Measurements of plant physiology are crucial to the calibration of Dynamic Global Vegetation Models (DGVMs) that are currently used to simulate the responses of vegetation in face of global changes. In a field work realized in an area of preserved Caatinga forest located in Petrolina, Pernambuco, measurements of carbon assimilation (in response to light and CO2) were performed on 11 individuals of Poincianella microphylla, a native species that is abundant in this region. These data were used to calibrate the maximum carboxylation velocity (Vcmax) used in the INLAND model. The calibration techniques used were Multiple Linear Regression (MLR), and data mining techniques as the Classification And Regression Tree (CART) and K-MEANS. The results were compared to the UNCALIBRATED model. It was found that simulated Gross Primary Productivity (GPP) reached 72% of observed GPP when using the calibrated Vcmax values, whereas the UNCALIBRATED approach accounted for 42% of observed GPP. Thus, this work shows the benefits of calibrating DGVMs using field ecophysiological measurements, especially in areas where field data is scarce or non-existent, such as in the Caatinga.

Resumo A região semiárida do nordeste do Brasil, a Caatinga, é extremamente importante devido à sua biodiversidade e endemismo. Medidas de fisiologia vegetal são cruciais para a calibração de Modelos de Vegetação Globais Dinâmicos (DGVMs) que são atualmente usados para simular as respostas da vegetação diante das mudanças globais. Em um trabalho de campo realizado em uma área de floresta preservada na Caatinga localizada em Petrolina, Pernambuco, medidas de assimilação de carbono (em resposta à luz e ao CO2) foram realizadas em 11 indivíduos de Poincianella microphylla, uma espécie nativa que é abundante nesta região. Estes dados foram utilizados para calibrar a velocidade máxima de carboxilação (Vcmax) usada no modelo INLAND. As técnicas de calibração utilizadas foram Regressão Linear Múltipla (MLR) e técnicas de mineração de dados como Classification And Regression Tree (CART) e K-MEANS. Os resultados foram comparados com o modelo INLAND não calibrado. Verificou-se que a Produtividade Primária Bruta (PPB) simulada atingiu 72% da PPB observada ao usar os valores de Vcmax calibrado, enquanto que o modelo não calibrado obteve-se 42% da PPB observada. Assim, este trabalho mostra os benefícios de calibrar DGVMs usando medidas ecofisiológicas de campo, especialmente em áreas onde os dados de campo são escassos ou inexistentes, como na Caatinga.

Componente para la toma de decisiones en salud. Un enfoque de análisis de redes sociales desde la minería de procesos / Component to decision making in health. A social network analisis approach from process mining

La mayoría de los sistemas informáticos en la actualidad generan trazas. Estas trazas revelan las acciones que son ejecutadas en estos sistemas. La Minería de Procesos tiene como objetivo descubrir, monitorear y mejorar los procesos reales de las organizaciones a través de la extracción de conocimiento de estas trazas, luego de aplicadas un conjunto de transformaciones para organizar, estructurar y limpiar la información. Sin embargo, esto no es posible si estos sistemas informáticos y sus organizaciones no tienen sus acciones con un enfoque basado en procesos. El uso de estas tecnologías permite ahorrar recursos, reducir costos, optimizar tareas, mejorar la productividad, reducir tiempos de espera, entre otras muchas acciones. En el sector de la salud es una necesidad inmediata en términos de proveer una mayor seguridad al paciente y mejorar la calidad de vida. El objetivo de esta investigación es presentar un componente para la toma de decisiones en la selección de equipos de trabajo quirúrgico en un Sistema de Información Hospitalaria que permita incrementar la efectividad de las operaciones realizadas a los pacientes. El método utilizado es el enfoque de Análisis de Redes Sociales desde la Minería de Procesos. Como resultado se espera un componente que apoye la toma de decisiones por parte de jefes de servicios de cirugía, partiendo del desempeño profesional del personal asistencial, en función de proveer un mayor confort para el paciente(AU)

Most computer systems today generate traces. These traces show the actions that are executed in those systems. Process Mining aims to discover, to monitor and to improve real processes of organizations through knowledge extraction of these traces, after applying a set of transformations to organize, to structure and to clear this information. However, this is not possible if these computer systems and its organizations do not have their actions with a process-based approach. The use of these technologies allows saving resources, to reduce costs, to optimize tasks, to improve productivity, to reduce wait times, among many other actions. In the health sector is an immediate need in terms of providing greater patient safety and to improve quality of life. The objective of this research is to present a component for decision making in selection surgical teams work in a Hospital Information System that it allows to increase the effectiveness of operations performed to the patients. The method used is a Social Network Analysis approach from the Process Mining As a result it expected a component to support decision making by managers surgical and psychological personnel starting from the professional performance of health care personnel in function of providing greater comfort to the patient(AU)

Lacaziosis - unusual clinical presentation

Lacaziosis or Jorge Lobo's disease is a fungal, granulomatous, chronic infectious disease caused by Lacazia loboi, which usually affects the skin and subcutaneous tissue. It is characterized by slow evolution and a variety of cutaneous manifestations with the most common clinical expression being nodular keloid lesions that predominate in exposed areas. We report the case of a patient who had an unusual clinical presentation, with a single-plated lesion on the back. Histopathological examination confirmed the diagnosis of Lacaziosis.

Componente web para el análisis de información clínica usando la técnica de Minería de Datos por agrupamiento / Web component for the analysis of clinical information using the technique of clustering data mining

La digitalización de los diferentes procesos y la automatización de los servicios generan grandes volúmenes de información. La Minería de Datos (MD) es una técnica de Inteligencia Artificial que permite encontrar la información no trivial que reside en los datos almacenados. La presente investigación pretende desarrollar una vista de análisis para el Sistema Integral para la Atención Primaria de Salud (SIAPS), usando la técnica de agrupamiento enmarcada en el algoritmo Simple K-Means, con el objetivo de realizar un análisis de la información clínica de los pacientes; para ello se plantea la extracción del conocimiento del almacén de datos alimentado del repositorio de historias clínicas electrónicas. La investigación se sustenta en la herramienta de libre distribución WEKA, esta funciona de forma aislada al SIAPS; la interfaz, así como las vistas, modelos e informes generados por WEKA en ocasiones resultan de difícil comprensión por los profesionales de la salud, los que no necesariamente tienen que poseer conocimientos avanzados de las nuevas tecnologías de la información. Para el desarrollo de la solución se empleó el lenguaje de programación Java 1.6, como servidor de aplicación JBoss 4.2 y Eclipse 3.4 como plataforma de desarrollo, como Sistema Gestor de Bases de Datos PostgreSQL 8.4 y SEAM como framework de integración. Durante todo el proceso se hizo uso de la plataforma Java Enterprise Edition 5.0. Como resultado se espera obtener una vista de análisis que facilite la comprensión de los modelos generados, apoyando de esta forma el proceso de toma de decisiones clínicas(AU)

The digitization of the different processes and automation services generate large volumes of information. Data mining (DM) is an artificial intelligence technique that allows finding non-trivial information residing in stored data. This research aims to develop a view of analysis for the Integral System for Primary Health Care (SIAPS), using grouping technique framed on Simple K-Means algorithm, with the goal of completing an analysis of the patients' clinical information, for it raises the extraction of knowledge from data warehouse powered by the repository of electronic medical records. The research is based on the free distribution tool WEKA, it works in isolation of SIAPS, the interface, as well as the views, models and reports generated by WEKA are sometimes difficult to understand by health professionals, who do not necessarily have to possess advanced knowledge of new information technologies. For the development of the solution was used Java 1.6 as a programming language, JBoss 4.2 as the application Server and Eclipse 3.4 as a development platform. PostgreSQL 8.4 was used as Database Management System and the integration framework SEAM. Java Enterprise Edition 5.0 platform was used during the whole process. An analysis view to facilitate the understanding of the generated models is expected as a result, to support the process of making clinical decisions(AU)

Método de mineração de dados para identificação de câncer de mama baseado na seleção de variáveis / A data mining method for breast cancer identification based on a selection of variables

Na maioria dos países, o câncer de mama entre as mulheres é predominante. Se diagnosticado precocemente, apresenta alta probabilidade de cura. Diversas abordagens baseadas em Estatística foram desenvolvidas para auxiliar na sua detecção precoce. Este artigo apresenta um método para a seleção de variáveis para classificação dos casos em duas classes de resultado, benigno ou maligno, baseado na análise citopatológica de amostras de célula da mama de pacientes. As variáveis são ordenadas de acordo com um novo índice de importância de variáveis que combina os pesos de importância da Análise de Componentes Principais e a variância explicada a partir de cada componente retido. Observações da amostra de treino são categorizadas em duas classes através das ferramentas k-vizinhos mais próximos e Análise Discriminante, seguida pela eliminação da variável com o menor índice de importância. Usa-se o subconjunto com a máxima acurácia para classificar as observações na amostra de teste. Aplicando ao Wisconsin Breast Cancer Database, o método proposto apresentou uma média de 97,77% de acurácia de classificação, retendo uma média de 5,8 variáveis.

In the majority of countries, breast cancer among women is highly prevalent. If diagnosed in the early stages, there is a high probability of a cure. Several statistical-based approaches have been developed to assist in early breast cancer detection. This paper presents a method for selection of variables for the classification of cases into two classes, benign or malignant, based on cytopathological analysis of breast cell samples of patients. The variables are ranked according to a new index of importance of variables that combines the weighting importance of Principal Component Analysis and the explained variance based on each retained component. Observations from the test sample are categorized into two classes using the k-Nearest Neighbor algorithm and Discriminant Analysis, followed by elimination of the variable with the index of lowest importance. The subset with the highest accuracy is used to classify observations in the test sample. When applied to the Wisconsin Breast Cancer Database, the proposed method led to average of 97.77% in classification accuracy while retaining an average of 5.8 variables.

Phenotypic spectrum in uniparental disomy: Low incidence or lack of study.

CONTEXT: Alterations in the human chromosomal complement are expressed phenotypically ranging from (i) normal, via (ii) frequent fetal loss in otherwise normal person, to (iii) sub‑clinical to severe mental retardation and dysmorphism in live births. A subtle and microscopically undetectable chromosomal alteration is uniparental disomy (UPD), which is known to be associated with distinct birth defects as per the chromosome involved and parental origin. UPD can be evident due to imprinted genes and/or activation of recessive mutations. AIMS: The present study comprises of data mining of published UPD cases with a focus on associated phenotypes. The goal was to identify non‑random and recurrent associations between UPD and various genetic conditions, which can possibly indicate the presence of new imprinted genes. SETTINGS AND DESIGN: Data mining was carried out using the homepage “http://www.fish.uniklinikum‑jena.de/ UPD.html,” an online catalog of published cases with UPD. MATERIALS AND METHODS: The UPD cases having normal karyotype and with or without clinical findings were selected to analyze the associated phenotypes for each chromosome, maternal or paternal involved in UPD. RESULTS: Our results revealed many genetic conditions (other than the known UPD syndromes) to be associated with UPD. Even in cases of bad obstetric history as well as normal individuals chance detection of UPD has been reported. CONCLUSIONS: The role of UPD in human genetic disorders needs to be studied by involving larger cohorts of individuals with birth defects as well as normal population. The genetic conditions were scrutinized in terms of inheritance patterns; majority of these were autosomal recessive indicating the role of UPD as an underlying mechanism.

Técnicas de minería de datos aplicadas al diagnóstico de entidades clínicas / Data mining techniques aplied to diagnosys of clinical entities

Disminuir el error médico y mejorar los procesos de salud es prioridad de todo el personal sanitario. En este contexto surgen los Sistemas Clínicos de Soporte para la Toma de Decisiones (CDSS), los cuales son un componente fundamental en la informatización de la capa clínica. Con la evolución de las tecnologías gran cantidad de datos han podido ser estudiados y clasificados a partir de la minería de datos. Una de las principales ventajas de la utilización de esta, en los CDSS, ha sido su capacidad de generar nuevos conocimientos. Con este fin se propone, mediante la combinación de dos modelos matemáticos, cómo se puede contribuir al diagnóstico de enfermedades usando técnicas de minería de datos. Para mostrar los modelos utilizados se tomó como caso de estudio la hipertensión arterial. El desarrollo de la investigación se rige por la metodología más utilizada actualmente en los procesos de Descubrimiento de Conocimiento en Bases de Datos: CRISP-DM 1.0, y se apoya en la herramienta de libre distribución WEKA 3.6.2, de gran prestigio entre las utilizadas para el modelado de minería de datos. Como resultados se obtuvieron diversos patrones de comportamiento con relación a los factores de riesgo a sufrir hipertensión mediante técnicas de minería de datos(AU)

Reduce medical errors and improve health processes is a priority of all health personnel. In this context arise the Clinical Support Systems for Decision Making (CDSS), which are a key component in computerization of the clinical layer. With the evolution of technologies, large amounts of data have been studied and classified based on data mining. One of the main advantages of using this in the CDSS, has been its ability to generate new knowledge. For this purpose, this paper presents, by combining two mathematical models, a way to contribute to the diagnosis of diseases using data mining techniques. Hypertension was taken as a case study to show the models used. The research development methodology follows the most used processes of knowledge discovery in databases: CRISP-DM 1.0, and relies on the free distribution tool WEKA 3.6.2. We obtained different patterns of behavior in relation to risk factors for developing hypertension using data mining techniques(AU)

Proposta de reflexão teórica e análise de padrões conceituais com data mining / Theoretical discussion and conceptual pattern analysis with data mining

Mais do que uma teoria ou modelo, a Teoria da Mente se refere a um campo de estudos voltado à habilidade de se prospectar intenções alheias. Visando contribuir para a discussão teórica e a interpretação da literatura no tema, o presente estudo apresenta: 1. Um mapa conceitual do campo, baseado em data mining/text mining; 2. Uma abordagem conceitual inovadora e mais eficiente aos estudos de ToM informacional; 3. Uma discussão crítica da extensão e limites dos principais modelos, baseada na análise da literatura com data/text mining e nas perspectivas teóricas anteriormente alinhavadas.

More than just a theory or a model, Theory of Mind represents a field of studies concerned with the ability to prospect someone else's intentions. Aiming to contribute to theoretical discussion and the interpretation of the literature on the matter, this study presents: 1. A conceptual map of the field, based on data mining/text mining techniques; 2. A new and advanced conceptual framework focused on informational ToM studies; 3. A critical discussion of the extensions and limits of the most prominent models, based on the outputs of the data/text mining analysis and on the theoretical perspectives that were previously raised.