RESUMO

Ecotechnology, quintessential for crafting sustainable socio-environmental strategies, remains tantalizingly uncharted. Our analysis, steered by the nuances of machine learning and augmented by bibliometric insights, delineates the expansive terrain of this domain, elucidates pivotal research themes and conundrums, and discerns the vanguard nations in this field. Furthermore, we deftly connect our discoveries to the United Nations' 2030 Sustainable Development Goals, thereby accentuating the profound societal ramifications of ecotechnology.

RESUMO

The aim of this study is to automatically analyze, characterize and classify physical performance and body composition data of a cohort of Mexican community-dwelling older adults. Self-organizing maps (SOM) were used to identify similar profiles in 562 older adults living in Mexico City that participated in this study. Data regarding demographics, geriatric syndromes, comorbidities, physical performance, and body composition were obtained. The sample was divided by sex, and the multidimensional analysis included age, gait speed over height, grip strength over body mass index, one-legged stance, lean appendicular mass percentage, and fat percentage. Using the SOM neural network, seven profile types for older men and women were identified. This analysis provided maps depicting a set of clusters qualitatively characterizing groups of older adults that share similar profiles of body composition and physical performance. The SOM neural network proved to be a useful tool for analyzing multidimensional health care data and facilitating its interpretability. It provided a visual representation of the non-linear relationship between physical performance and body composition variables, as well as the identification of seven characteristic profiles in this cohort.

Assuntos

RESUMO

INTRODUCTION: Scientometrics analyzes scientific publications through bibliometric and computational techniques, whereby productivity and impact indicators are generated. OBJECTIVE: To propose a multidimensional methodology in order to obtain the scientometric profile of the National Cancer Institute (INCan), Mexico, and rank it with regard to other national health institutions. METHOD: Using the LabSOM software and the ViBlioSOM methodology based on artificial neural networks, the INCan scientific production indexed in the Web of Science from 2007 to 2017 was analyzed. The multidimensional scientometric profile of the Institute was obtained and compared with that of other national health institutions. RESULTS: In terms of productivity, INCan ranks fourth among the 10 Mexican public health institutions indexed in the Web of Science; in the normalized impact ranking, it ranks sixth. Although out of 1323 articles 683 (51.62 %) did not receive citations, 11 articles classified as excellent (0.83 %) obtained 24 % of 11,932 citations and, consequently, INCan normalized impact rate showed a mean productivity higher than the world mean. CONCLUSION: Multidimensional analysis with the proposed neural network enables obtaining a more reliable and comprehensive absolute and relative institutional scientiometric profile than that derived from measuring isolated variables.

INTRODUCCIÓN: La cienciometría permite analizar la productividad e impacto de las publicaciones científicas mediante técnicas bibliométricas y computacionales. OBJETIVO: Proponer una metodología multidimensional para obtener el perfil cienciométrico del Instituto Nacional de Cancerología (INCan), México, y compararlo respecto a otras instituciones nacionales de salud. MÉTODO: Con el programa LabSOM y la metodología ViBlioSOM, basada en redes neuronales artificiales, se analizó la producción científica del INCan indexada en la Web of Science entre 2007 y 2017. Se obtuvo el perfil cienciométrico multidimensional del Instituto y se comparó con el de otras instituciones nacionales de salud. RESULTADOS: En productividad, el INCan ocupa el cuarto lugar de las 10 instituciones mexicanas de salud pública indexadas en la Web of Science.; en el ranking de impacto normalizado, el sexto lugar. Aun cuando de 1323 artículos, 683 (51.62 %) no recibieron citas, 11 artículos de excelencia (0.83 %) lograron 24 % de 11 932 citas y, consecuentemente, el impacto normalizado del INCan evidenció una productividad media por arriba de la media mundial. CONCLUSIÓN: El análisis multidimensional con la red neuronal propuesta permite obtener un perfil cienciométrico institucional absoluto y relativo más fidedigno e integral que el derivado de conteos de variables aisladas.

Assuntos

RESUMO

Resumen Introducción: La cienciometría permite analizar la productividad e impacto de las publicaciones científicas mediante técnicas bibliométricas y computacionales. Objetivo: Proponer una metodología multidimensional para obtener el perfil cienciométrico del Instituto Nacional de Cancerología (INCan), México, y compararlo respecto a otras instituciones nacionales de salud. Método: Con el programa LabSOM y la metodología ViBlioSOM, basada en redes neuronales artificiales, se analizó la producción científica del INCan indexada en la Web of Science entre 2007 y 2017. Se obtuvo el perfil cienciométrico multidimensional del Instituto y se comparó con el de otras instituciones nacionales de salud. Resultados: En productividad, el INCan ocupa el cuarto lugar de las 10 instituciones mexicanas de salud pública indexadas en la Web of Science.; en el ranking de impacto normalizado, el sexto lugar. Aun cuando de 1323 artículos, 683 (51.62 %) no recibieron citas, 11 artículos de excelencia (0.83 %) lograron 24 % de 11 932 citas y, consecuentemente, el impacto normalizado del INCan evidenció una productividad media por arriba de la media mundial. Conclusión: El análisis multidimensional con la red neuronal propuesta permite obtener un perfil cienciométrico institucional absoluto y relativo más fidedigno e integral que el derivado de conteos de variables aisladas.

Abstract Introduction: Scientometrics analyzes scientific publications through bibliometric and computational techniques, whereby productivity and impact indicators are generated. Objective: To propose a multidimensional methodology in order to obtain the scientometric profile of the National Cancer Institute (INCan), Mexico, and rank it with regard to other national health institutions. Method: Using the LabSOM software and the ViBlioSOM methodology based on artificial neural networks, the INCan scientific production indexed in the Web of Science from 2007 to 2017 was analyzed. The multidimensional scientometric profile of the Institute was obtained and compared with that of other national health institutions. Results: In terms of productivity, INCan ranks fourth among the 10 Mexican public health institutions indexed in the Web of Science; in the normalized impact ranking, it ranks sixth. Although out of 1323 articles 683 (51.62 %) did not receive citations, 11 articles classified as excellent (0.83 %) obtained 24 % of 11,932 citations and, consequently, INCan normalized impact rate showed a mean productivity higher than the world mean. Conclusion: Multidimensional analysis with the proposed neural network enables obtaining a more reliable and comprehensive absolute and relative institutional scientiometric profile than that derived from measuring isolated variables.

Assuntos

RESUMO

La relación de la tuberculosis, el Bacillus Calmette-Guérin y las vacunas de tuberculosis como dominio bajo estudio, parte del hecho de que la única vacuna disponible hoy para prevenir la tuberculosis en humanos es la BCG, y el mejoramiento de ella o el desarrollo de nuevas vacunas es estratégico para el control de esta enfermedad. Este estudio pretende contribuir con estas importantes investigaciones a partir de los estudios patentométricos, y tiene como objetivo realizar un análisis métrico que permita describir la productividad de patentes sobre tuberculosis, Bacillus Calmette-Guérin y vacunas de tuberculosis en un determinado periodo de tiempo. Para el estudio de la productividad se analizó el comportamiento de indicadores temporales y geográficos en el dominio, en el que se utilizaron técnicas y herramientas apropiadas para los documentos de patentes. A la investigación de la tuberculosis como enfermedad infecciosa transmisible se le han dedicado grandes esfuerzos. La tuberculosis fue considerada hasta hace poco un problema de salud de los países en desarrollo, mientras hoy, con la reemergencia de la enfermedad, los países desarrollados han acaparado su investigación; sin embargo, estos esfuerzos no han sido proporcionales con la investigación dedicada a una nueva generación de vacunas contra esta enfermedad y no existen nuevas patentes que lo demuestren...

As an object of study, the relationship between tuberculosis, Bacillus Calmette-Guérin and tuberculosis vaccines starts from the fact that the only vaccine currently available to prevent tuberculosis in humans is BCG, and its improvement or the development of new vaccines is a key strategy to control the disease. The present study intends to make a contribution to such important research from a patent metrics perspective. Its purpose is to conduct a metric analysis allowing to describe the productivity of patents for tuberculosis, Bacillus Calmette-Guérin and tuberculosis vaccines in a given time period. For the productivity study, an analysis was carried out of the behavior of temporal and geographic indicators in the domain, using techniques and tools suitable for patent documents. Research into tuberculosis as an infectious communicable disease has received great attention. Until recently, tuberculosis was considered to be a health problem in the developing world. However, after its re-emergence, research has been mainly conducted in developed countries. But such efforts have not been in proportion to research aimed at developing a new generation of vaccines against the disease, and there are no new patents supporting them...

Assuntos

RESUMO

We evaluate the effectiveness of seven Bayesian network classifiers as potential tools for the diagnosis of breast cancer using two real-world databases containing fine-needle aspiration of the breast lesion cases collected by a single observer and multiple observers, respectively. The results show a certain ingredient of subjectivity implicitly contained in these data: we get an average accuracy of 93.04% for the former and 83.31% for the latter. These findings suggest that observers see different things when looking at the samples in the microscope; a situation that significantly diminishes the performance of these classifiers in diagnosing such a disease.

Assuntos