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Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: The rising global population and decreasing agricultural land challenge the food supply, worsened by land degradation and water scarcity. Using IoT solutions, precision farming offers promise for addressing these issues by enhancing productivity, profitability, and environmental sustainability. The paper proposes an IoT-based automated monitoring and irrigation system with predictive analysis as a solution. Aims: To develop an IoT-based irrigation model with cloud computing, a mobile app for remote monitoring and irrigation control, and a predictive analysis method for forecasting weather conditions. Methods: IoT sensors are connected to a node MCU, and they monitor real-time temperature, humidity, and soil moisture. A water pump adjusts irrigation accordingly. Data is sent to ThingSpeak for visualization and Firebase for storage and is accessible via a mobile app. Predictive analysis combines sensor and historical weather data using the Random Forest algorithm to optimize irrigation. This determines the ideal irrigation frequency, duration, and timing based on predicted temperature and soil moisture levels, ensuring optimal soil moisture for plant growth. Results: The system exhibited encouraging outcomes. It displayed transformative potential by harnessing IoT, cloud computing, and predictive analytics. Offering precise soil moisture monitoring, instant data access, and tailored advice, the system can elevate crop management, streamline water consumption, and boost productivity among Indian farmers. Discussion: The automated crop monitoring and irrigation system has transformative potential in farming. Some future scope and enhancements may include broadening crop compatibility, integrating satellite data, enhancing pest monitoring, improving connectivity, enriching the mobile app, scaling up through partnerships, and refining the predictive models. Conclusions: The IoT-driven crop monitoring system revolutionizes Indian agriculture by optimizing irrigation and integrating weather forecasts. With mobile app enhancements and future improvements, it promises sustainable farming and empowerment for farmers. [ABSTRACT FROM AUTHOR]

Introduction: The lengthy and costly process of drug development can be expedited through drug repositioning (DR), a strategy that identifies new therapeutic targets using existing products. Supervised machine learning (SML) models, incorporating interaction networks, offer a promising approach for DR. This study aims to systematically review and meta-analyze SML models predicting DR, identifying key characteristics influencing their performance. Methodology: A systematic review was conducted to identify SML models that used networks to predict DR, which were evaluated by comparing their performance through a random-effects meta-analysis. Results: 19 studies were included in the qualitative synthesis and 17 in the quantitative evaluation, The Random Forest (RF) model emerged as the predominant classifier (63%), yielding the highest performance in AUC ROC comparisons (overall value: 0.91, 95% CI: 0.86 - 0.96). Validation efforts in 18 studies confirmed the predictions of the SML models, affirming the proposed drugs. The incorporation of chemical structure in model training was found to enhance performance by aiding in prediction discrimination. Conclusion: SML models can predict DR, the RF model was the most widely used SML model with the best performance results, which underscores the potential use of FR models for predicting DR using network form biomedical information. [ABSTRACT FROM AUTHOR]

Copyright of Arquivos Brasileiros de Oftalmologia is the property of Arquivos Brasileiros de Oftalmologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

O Cerrado e o Distrito Federal (DF) sofrem com o processo de fragmentação. O lançamento dos nanosatélites PlanetScope representa uma oportunidade de levar o monitoramento ambiental desse bioma a um novo patamar. Nesse estudo, utilizamos mosaicos mensais das estações seca e chuvosa e aplicamos o método de classificação Random Forest. As áreas de treinamento foram coletadas em campo com o uso de uma Aeronave Remotamente Pilotada (RPA). Os resultados mostraram alta precisão na identificação de diferentes tipos de cobertura vegetal (índice Kappa ? 0,835 e índice de similaridade 0,901). Os modelos com mais variáveis apresentaram maior precisão e as imagens classificadas conseguiram captar mudanças na cobertura do solo relacionadas à dinâmica agrícola. Além disso, a importância de cada banda no resultado das classificações pôde ser analisada

Despite improved higher education accessibility in low and middle-income countries (LMICs), challenges persist in student drop-out, especially for socio-economically disadvantaged students. While machine learning models have enhanced our understanding of this challenge by predicting academic performance, many studies overlook LMIC-specific institutional factors or focus on specific courses, limiting their generalizability and policy uses. To address these issues, the authors compiled a comprehensive database using administrative and census data to predict undergraduate academic performance at the Pontifical Catholic University of Peru (PUCP). The study found that the most effective models were tree-based ensembles, particularly Random Forest, with key predictors including prior secondary school performance and university admission test scores. They present a high-performing model using only ten features that can predict future academic performance and potentially aid in reducing student drop-out at PUCP. [ABSTRACT FROM AUTHOR]

El Cerrado, la sabana brasileña, sufre el proceso de fragmentación en el Distrito Federal (DF). El lanzamiento de los nanosatélites PlanetScope representa una oportunidad para llevar el monitoreo ambiental de este bioma a un nuevo nivel. En este estudio, utilizamos mosaicos mensuales de las estaciones seca y lluviosa y aplicamos el método de clasificación Random Forest. Las áreas de entrenamiento se recogieron en campo utilizando una Aeronave Pilotada Remotamente (RPA). Los resultados mostraron alta precisión en la identificación de diferentes tipos de cobertura vegetal (índice Kappa = 0,835 y índice de similitud 0,901). Los modelos con más variables mostraron mayor precisión y las imágenes clasificadas pudieron capturar cambios en la cobertura del suelo relacionados con la dinámica agrícola. Tambien se pudo analizar la importancia de cada banda en el resultado de las clasificaciones. [ABSTRACT FROM AUTHOR]

O artigo discute quais ferramentas filosóficas são indispensáveis e fundamentais para a compreensão do significado da tecnologia nos tempos modernos. Centra-se no debate sobre a inteligência artificial “substituindo o pensamento”. Fornece critérios filosóficos para avaliar a ruptura entre humanismo e tecnologia e possíveis escolhas interpretativas e analíticas sobre a cognição humana e a possibilidade de sua duplicação por máquinas. Levanta questões éticas e jurídicas nos debates atuais envolvendo Inteligência Artificial e Filosofia, no sentido da necessidade de regulamentar o uso da inteligência artificial de forma ética e legal, considerando a perspectiva humana de seu uso e os impactos para a humanidade. Pondera se surgirá uma filosofia de reconstrução do mundo baseada em padrões tecnológicos ou se recorrerá à tradição humanista para desenvolver uma interpretação, dado que as relações entre humanos e máquinas ainda não são claras. [ABSTRACT FROM AUTHOR]

Changes in vegetation cover due to increasing frequencies of extreme climate events and anthropogenic pressure are already underway; so, predicting the impacts of the near‐future climate will be essential for developing mitigation strategies. We modelled the responses of Brazilian biomes to a future scenario (2070) of steady increases in atmospheric CO levels, adding soil data to better represent the multidimensional space of the environmental suitability of each biome. We also assessed the effects of changes in environmental suitability on the Brazilian network of protected areas and projected those effects on 1 km resolution maps. The area predicted to be affected by future climate change in Brazil and the consequent loss of suitable habitat surface is 2.59 Mkm2 – larger than the combined areas of Central America and Mexico – leading the current vegetation to a progressive replacement. We project major changes in the vegetation of the Amazon basin, with the replacement of rainforest by dryer vegetation in the southern and eastern regions of that basin, and the opening of a dry corridor in Pará State. We also project an expansion of 41% of the current caatinga cover in the Brazilian semiarid region, with large losses of suitable habitat surface of the current deciduous forest. Approximately, 37% of the coverage of protected areas in Brazil will be affected – with greater damage to indigenous lands. The speed of current environmental change is now unprecedented for the post‐glacial era, and will almost certainly lead to increased rates of extinction and the collapse of transition ecosystems. We propose the urgent creation of protected areas in regions designed without significant impacts, but contiguous to those that will be more seriously affected by climate change. Those areas will act as refugia preserving biodiversity, ecosystem services, and the cultural heritages of traditional populations. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ciência & Saúde Coletiva is the property of Associacao Brasileira de Pos-Graduacao em Saude Coletiva and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Synesis is the property of Synesis and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Gestão & Tecnologia is the property of Revista Gestao & Tecnologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introdução: O câncer de próstata é o segundo mais comum entre os homens no Brasil, atrás apenas do câncer de pele não melanoma. Atualmente, há interesse em analisar dados referentes ao câncer com métodos do tipo machine learning. Objetivo: Investigar as características físicas, socioeconômicas e de hábitos de vida que podem estar associadas ao diagnóstico de câncer de próstata no Brasil. Método: Uma base de microdados referente à Pesquisa Nacional de Saúde 2019 foi utilizada, com a seleção de 42.799 indivíduos do sexo masculino; esse grupo foi analisado por meio de métodos estatísticos e modelagem por machine learning (regressão logística e árvore de decisão). Resultados: Os modelos aplicados permitiram identificar com bom nível de acurácia (próximo ou acima de 80%) os indivíduos que receberam o diagnóstico de câncer de próstata (DCP), além de grupos com características específicas mais fortemente associadas a essa doença. Entre as variáveis mais significativamente ligadas à taxa de DCP, destacam-se: idade, diagnóstico de alto nível de colesterol, se possui plano de saúde e nível de instrução. Conclusão: Os modelos indicam um nível de associação significativo de fatores socioeconômicos, físicos e alimentares com a frequência de DCP no grupo analisado. O alto nível de acurácia e a sensibilidade dos modelos demonstram o potencial dos métodos de machine learning para a previsão de DCP.

ABSTRACT Introduction. Artificial intelligence and machine learning are technologies that assist in uncovering patterns in data that can inform clinical decision-making. The Registered Nurses’ Association of Ontario has used artificial intelligence techniques to assist in understanding impactful clinical practices and implementation strategies. This scoping review aimed to discover the adaptation and implementation of various artificial intelligence and machine learning techniques in various healthcare settings using different data systems that house nursing-related data. Methodology. In March 2022, a scoping review was conducted to search for peer-reviewed literature using the following terms: “nursing”, “artificial intelligence”, “data systems”, “statistics”, and “aggregated data”. Studies were excluded if they were not relevant to nursing, utilized qualitative or mixed-methods analyses, were literature review articles, and did not focus on artificial intelligence or the use of patient-level data. Results. A total of 2,627 articles were retrieved, with 1,518 articles remaining after de-duplication. Through title and abstract screening, 1,347 articles remained. Following the full-text screening, 13 studies remained. Artificial intelligence techniques used by healthcare data systems include regression, neural networks, classification, and graph-based methods, among others. Discussion. There is a gap in the application of artificial intelligence methods in data systems that evaluate the impact of implementing best practices in nursing. More data systems are needed that employ artificial intelligence techniques to support the evaluation of best practices in nursing and other health professions. Conclusions. Various artificial intelligence techniques in data systems housing nursing-related data were retrieved. However, more data systems and research are needed in this area. Keywords: Practice Guidelines as Topic; Evidence-Based Nursing; Machine Learning; Artificial Intelligence; Health Information Systems RESUMEN Introducción. La inteligencia artificial y el aprendizaje automático son tecnologías que ayudan a descubrir patrones en los datos que pueden informar la toma de decisiones clínicas. La Asociación de Enfermeras Registradas de Ontario ha utilizado técnicas de inteligencia artificial para ayudar a comprender las prácticas clínicas que generan impacto y las estrategias de implementación. El objetivo de esta revisión es descubrir la adaptación e implementación de diversas técnicas de inteligencia artificial y aprendizaje automático en varios entornos sanitarios, utilizando diferentes sistemas de datos que almacenan datos relacionados con la enfermería. Metodología. En marzo de 2022, se realizó una revisión de alcance para buscar literatura revisada por pares utilizando los siguientes términos: «enfermería», «inteligencia artificial», «sistemas de datos», «estadística» y «datos agregados». Se excluyeron los estudios si no eran relevantes para la enfermería, utilizaban análisis cualitativos o de métodos mixtos, si eran artículos de revisión bibliográfica y no se centraban en la inteligencia artificial o en el uso de datos a nivel de paciente. Resultados. Se recuperó un total de 2,627 artículos, de los cuales 1,518 quedaron tras la eliminación de duplicados. Tras la revisión de títulos y resúmenes, quedaron 1,347 artículos. Posteriormente, con la revisión del texto completo, quedaron 13 estudios. Las técnicas de inteligencia artificial utilizadas por los sistemas de datos sanitarios incluyen, entre otras, la regresión, las redes neuronales, la clasificación y los métodos basados en gráficos. Discusión. Existe un vacío en la aplicación de métodos de inteligencia artificial en los sistemas de datos que evalúan el impacto de la implementación de buenas prácticas en enfermería. Se necesitan más sistemas de datos que empleen técnicas de inteligencia artificial para apoyar la evaluación de buenas prácticas en enfermería y otras profesiones de la salud. Conclusiones. Se recuperaron diversas técnicas de inteligencia artificial en sistemas de datos que almacenan datos relacionados con la enfermería. Sin embargo, se necesitan más sistemas de datos e investigación en este ámbito. Palabras clave: Guías de Práctica Clínica como Asunto; Enfermería Basada en la Evidencia; Aprendizaje Automático; Inteligencia Artificial; Sistemas de Información en Salud RESUMO Introdução. A inteligência artificial e o aprendizado de máquina são tecnologias que ajudam a descobrir padrões em dados que podem informar a tomada de decisões clínicas. A Associação de Enfermeiras Registradas de Ontário vem utilizando técnicas de inteligência artificial para ajudar a entender as práticas clínicas que geram impacto e as estratégias de implementação. O de máquina em diversos ambientes de saúde, utilizando diferentes sistemas de dados que armazenam dados relacionados à enfermagem. Metodologia. Em março de 2022, foi realizada uma revisão de escopo para pesquisar literatura revisada por pares usando os seguintes termos: «enfermagem», «inteligência artificial», «sistemas de dados», «estatísticas» e «dados agregados». Foram excluídos os estudos que não se mostravam relevantes para a enfermagem, utilizavam análises qualitativas ou de métodos mistos, se eram de artigos de revisão de literatura e não focavam na inteligência artificial ou no uso de dados no nível do paciente. Resultados. Foram recuperados 2,627 artigos no total, dos quais 1,518 permaneceram após a eliminação das duplicatas. Após a revisão de títulos e resumos, restaram 1,347 artigos. Posteriormente, com a revisão do texto completo, restaram 13 estudos. As técnicas de inteligência artificial usadas pelos sistemas de dados de saúde incluem, entre outras, regressão, redes neurais, classificação e métodos baseados em gráficos. Discussão. Existe uma lacuna na aplicação de métodos de inteligencia artificial em sistemas de dados que avaliam o impacto da implementação de boas práticas de enfermagem. São necessários mais sistemas de dados que implementem técnicas de inteligência artificial para apoiar a avaliação de boas práticas em enfermagem e outras profissões de saúde. Conclusões. Diversas técnicas de inteligência artificial foram recuperadas em sistemas de dados que armazenam dados relacionados à enfermagem. No entanto, são necessários mais sistemas de dados e investigação nesta área. Palavras-chave: Guias de Prática Clínica como Assunto; Enfermagem Baseada em Evidências; Aprendizado de Máquina; Inteligência Artificial; Sistemas de Informação em Saúde

In recent decades, there have been significant advances in the diagnosis of diffuse gliomas, driven by the integration of novel technologies. These advancements have deepened our understanding of tumor oncogenesis, enabling a more refined stratification of the biological behavior of these neoplasms. This progress culminated in the fifth edition of the WHO classification of central nervous system (CNS) tumors in 2021. This comprehensive review article aims to elucidate these advances within a multidisciplinary framework, contextualized within the backdrop of the new classification. This article will explore morphologic pathology and molecular/genetics techniques (immunohistochemistry, genetic sequencing, and methylation profiling), which are pivotal in diagnosis, besides the correlation of structural neuroimaging radiophenotypes to pathology and genetics. It briefly reviews the usefulness of tractography and functional neuroimaging in surgical planning. Additionally, the article addresses the value of other functional imaging techniques such as perfusion MRI, spectroscopy, and nuclear medicine in distinguishing tumor progression from treatment-related changes. Furthermore, it discusses the advantages of evolving diagnostic techniques in classifying these tumors, as well as their limitations in terms of availability and utilization. Moreover, the expanding domains of data processing, artificial intelligence, radiomics, and radiogenomics hold great promise and may soon exert a substantial influence on glioma diagnosis. These innovative technologies have the potential to revolutionize our approach to these tumors. Ultimately, this review underscores the fundamental importance of multidisciplinary collaboration in employing recent diagnostic advancements, thereby hoping to translate them into improved quality of life and extended survival for glioma patients.

This article explores the application of machine learning algorithms in optimizing production costs within the supply chain. It focuses on a multinational flooring and sports surfaces company that operates in various countries. The study aims to address the challenges posed by the complexity of the production chain and the need for consistent product costs worldwide. By utilizing machine learning to analyze unstructured data stored in the company's servers, the study seeks to identify the most cost-effective production site, taking into account delivery costs. The article highlights the potential benefits of using machine learning in supply chain management, including improved efficiency and risk management. It also discusses the challenges and opportunities associated with utilizing dark data in supply chain analytics. Overall, the article emphasizes the positive impact of digital transformation and machine learning in optimizing supply chain operations. [Extracted from the article]

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Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Brazilian Journal of Education, Technology & Society (BRAJETS) / Cadernos de Educação Tecnologia e Sociedade (CETS) is the property of Brazilian Journal of Education, Technology & Society - BRAJETS and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Gestão & Tecnologia is the property of Revista Gestao & Tecnologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Enquadramento: a implementação de tecnologias baseadas em Inteligência Artificial (IA) na área da saúde, nomeadamente o machine learning (ML), tem causado um efeito transformacional significativo. A sua utilização melhora a previsão de doenças, classificação e diagnóstico, beneficiando os utentes e os profissionais de saúde. Objetivo: mapear as ferramentas de ML para a gestão de doenças crónicas, com relevância para os cuidados de enfermagem à pessoa com doença crónica. Metodologia: scoping review com base nas recomendações do Instituto Joanna Briggs. A pesquisa foi efetuada nas bases de dados MEDLINE Complete via PUBMED, CINAHL Complete via EBSCO, SCOPUS, OpenGrey, RCAAP e DART-Europe, sem limite de temporal. Resultados: foram incluídos sete artigos e identificadas 9 ferramentas de ML associados à gestão de doenças crónicas nomeadamente doença renal crónica, doença pulmonar obstrutiva crónica, hepatite C, insuficiência cardíaca e insuficiência venosa crónica. Conclusão: as ferramentas identificadas têm potencial de contribuir para a melhoria dos cuidados de enfermagem, nomeadamente na identificação de fatores de risco associados a doenças crónicas, detetar precocemente exacerbações, monitorizar e avaliar continuamente a eficácia do tratamento e apoiar a tomada de decisões clínicas.

Objective: Present an overview of scientific articles published in the last ten years on the topic of machine learning (ML), with an emphasis on predictive algorithms. Method/approach: Bibliometric analysis, with support from the PRISMA protocol, to evaluate authors, universities and countries, regarding productivity, bibliographic citations and focuses on the topic, with a sample of 773 articles from the Scopus and Web of Science databases, from 2013 to May /2023. Originality/value: There is an absence of studies in the literature that consolidate articles related to ML and Big Data. The research contributes to covering this gap, favoring the design of future actions and research. Main results: The following were identified in the ML bibliometric corpus: most cited authors with the greatest number of publications, most productive countries and universities, journals with the greatest number of publications and citations, areas of knowledge with the greatest number of publications, and the most prestigious articles. In the ML themes and domains, the following were identified: main co-occurrences of keywords, emerging themes (grouped into five clusters), and word clouds by title and abstract. Studies on the impact of data acquisition and predictive analysis represent opportunities for future research. Theoretical/methodological contributions: The PRISMA protocol enabled the identification and relevant quantitative and qualitative analyzes of articles, consolidating scientific knowledge on the topic. Social/managerial contributions: Ease of understanding the maturity of research on ML and Big Data by company managers and researchers, regarding the feasibility of investments to obtain competitive advantages with such technologies. [ABSTRACT FROM AUTHOR]

Objective: To validate a deep learning (DL) model for bone age estimation in individuals in the city of São Paulo, comparing it with the Greulich and Pyle method. Materials and Methods: This was a cross-sectional study of hand and wrist radiographs obtained for the determination of bone age. The manual analysis was performed by an experienced radiologist. The model used was based on a convolutional neural network that placed third in the 2017 Radiological Society of North America challenge. The mean absolute error (MAE) and the root-mean-square error (RMSE) were calculated for the model versus the radiologist, with comparisons by sex, race, and age. Results: The sample comprised 714 examinations. There was a correlation between the two methods, with a coefficient of determination of 0.94. The MAE of the predictions was 7.68 months, and the RMSE was 10.27 months. There were no statistically significant differences between sexes or among races (p > 0.05). The algorithm overestimated bone age in younger individuals (p = 0.001) Conclusion: Our DL algorithm demonstrated potential for estimating bone age in individuals in the city of São Paulo, regardless of sex and race. However, improvements are needed, particularly in relation to its use in younger patients. [ABSTRACT FROM AUTHOR]

Objective: To develop a convolutional neural network (CNN) model, trained with the Brazilian "Estudo Longitudinal de Saúde do Adulto Musculoesquelético" (ELSA-Brasil MSK, Longitudinal Study of Adult Health, Musculoskeletal) baseline radiographic examinations, for the automated classification of knee osteoarthritis. Materials and Methods: This was a cross-sectional study carried out with 5,660 baseline posteroanterior knee radiographs from the ELSA-Brasil MSK database (5,660 baseline posteroanterior knee radiographs). The examinations were interpreted by a radiologist with specific training, and the calibration was as established previously. Results: The CNN presented an area under the receiver operating characteristic curve of 0.866 (95% CI: 0.842-0.882). The model can be optimized to achieve, not simultaneously, maximum values of 0.907 for accuracy, 0.938 for sensitivity, and 0.994 for specificity. Conclusion: The proposed CNN can be used as a screening tool, reducing the total number of examinations evaluated by the radiologists of the study, and as a double-reading tool, contributing to the reduction of possible interpretation errors. [ABSTRACT FROM AUTHOR]

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Copyright of Ingeniería Solidaria is the property of Universidad Cooperativa de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista de Tecnologia Aplicada is the property of Faculdade Campo Limpo Paulista and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista do Serviço Público (Civil Service Review) is the property of Revista do Servico Publico and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Bioetica is the property of Conselho Federal de Medicina and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objetivo: investigar a literatura relacionada à aplicação e desempenho da Inteligência Artificial (IA) em exames de imagem odontológicos. Revisão de literatura: foram incluídos 70 trabalhos experimentais e revisões sistemáticas da literatura, publicados em inglês, no período entre 2018 e 2021, que analisaram a aplicabilidade da IA na detecção automática de: pontos cefalométricos, lesões de cárie, lesões apicais, perda óssea periodontal, sistemas de implantes, cistos e tumores odontogênicos, osteoporose, sinusite maxilar, terceiros molares e canal mandibular, ateromas em carótida, fratura radicular vertical, osteoartrite em articulação temporomandibular, avaliação de morfologia radicular e numeração dentária. Resultados: 58,73% dos trabalhos analisados mostrou acurácia diagnóstica acima de 80% com a utilização de IA. Discussão: A maior limitação encontrada foi em relação à aquisição de amostras em quantidade suficiente para treinamento e teste dos modelos, já que imagens radiográficas têm sua disponibilidade limitada por questões éticas e legais relativas aos pacientes e Instituições. A falta de padronização na segmentação e processamento das imagens foi outro fator a influenciar os resultados obtidos, dificultando comparação e generalização. Apesar disso, diversos estudos apresentaram sugestões ou possíveis aperfeiçoamentos para pesquisas futuras, de forma a reduzir estas limitações. Conclusão: A aplicação da IA no diagnóstico por imagens mostrou-se promissora nas diversas áreas pesquisadas, com desempenhos muito semelhantes ou mesmo superiores, muitas vezes, ao desempenho dos profissionais humanos. Contudo, para a legitimação de sua utilização como parte do fluxo de trabalho na clínica, limitações ainda presentes devem ser superadas, especialmente no treinamento dos algoritmos para obtenção de melhores valores de acurácia.

Copyright of Revista Facultad de Ingeniería - UPTC is the property of Universidad Pedagogica y Tecnologica de Colombia, Facultad de Ingenieria and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A necessidade de acompanhamento dos estágios de aprendizado discente e suas dificuldades de forma precoce tomou uma dimensão ainda maior nesses tempos recentes de pandemia de COVID-19 e de ensino on-line. Nesse panorama, este trabalho apresenta uma abordagem para predizer, de forma precoce, o desempenho de estudantes com probabilidade de reprovação superior a 50% em disciplinas específicas que ocorrem em ensino on-line. Para analisar e avaliar a viabilidade da abordagem proposta foi realizado um estudo de caso com dados do ensino técnico integrado do Campus Monteiro do IFPB a partir de dois cenários: (i) para turmas do primeiro ano e (ii) para turmas do segundo e terceiro anos. Foi construído um conjunto de dados com dados reais originados do Google Sala de Aula e do SUAP. Com base no conjunto de dados criado, foram construídos e avaliados modelos de classificação supervisionada usando os métodos Naive Bayes, KNN (K Nearest Neighbors), SVM (Support Vector Machine), Random Forest, Gradient Boosting e Extreme Gradient Boosting. Os métodos de classificação SVM e Gradient Boosting obtiveram especificidade superiores a 93% e 80%, respectivamente.

A evasão escolar é uma preocupação mundial devido às consequências negativas para toda a sociedade, sendo preciso investigá-la para compreendê-la e atuar de forma antecipada, mitigando seu risco de ocorrência. Esse trabalho propõe o emprego de Mineração de Dados Educacionais com técnicas de Aprendizado de Máquina para identificar as variáveis que são importantes para a caracterização do perfil do estudante em risco de evasão. As técnicas Máquina de Vetores de Suporte, Gradient Boosting Machine, Floresta Aleatória e comitê de máquina foram aplicadas a 1.429 registros de estudantes dos cursos superiores de um dos campi do IFMG, entre 2013 e 2019. Os resultados obtidos sugerem superioridade de desempenho do comitê de máquina, por meio do qual se obteve a importância das variáveis sobre o fenômeno em estudo, o que permitiu traçar o perfil do estudante evasor, por período. Tais resultados viabilizaram a proposição de um processo de detecção e acompanhamento desses estudantes.

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Cuadernos del Centro de Estudios de Diseño y Comunicación is the property of Cuadernos del Centro de Estudios de Diseno y Comunicacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The brazilian educational scenario is still characterized by high failure rates and sometimes recurrent traditional practices associated with what has been called "banking pedagogy". To help matters, active learning practices, which are growing as tools for enhancing engagement, interactivity and also school performance of students, have been considered. Some active learning practices are aimed at heterogeneous student classes. In this light, this work proposes an approach, called ActivePlan, which proposes to identify cognitive and interactional profiles of students in a class and, based on them, forms heterogeneous groups. The idea is organized by means of two aspects: (i) to group students at first by similar cognitive and interactional profiles by recognizing common knowledge and (ii) to form heterogeneous groups of students which are associated with some active learning practices (ALP). The ActivePlan approach has been evaluated by means of a real dataset composed by students from a technical integrated course and by considering the ALP Three Hundred. The Three Hundred ALP is based on the formation of groups of students that are potentially collaborative and, for this, considers different characteristics or roles of the members of the groups to facilitate collaboration. In this work, therefore, common knowledge associated with student performance (e.g., very high performance or low performance) has been used as a criterion for forming groups, both to identify similar competences (homogeneous grouping stage) and also to form groups with different profiles (heterogeneous formation stage). Through these identified common competences, groups with mixed profiles of students compatible with the practice Three hundred have been generated. The result of the formation of heterogeneous groups was evaluated using the diversity metric, whose average value was 3.3. This value, together with the analysis carried out, indicate that the approach brings promising results w.r.t. the formation of groups of students generated with regard to the practice Three hundred. [ABSTRACT FROM AUTHOR]

Land use and land cover maps are basic tools for a detailed understanding of spatial organization patterns, an essential tool for agro-environmental management. This article aimed to compare and evaluate the accuracy of the digital classification of land use and land cover for the Cerrado of Maranhão, using OLI (Operational Terra Imager) images from Landsat-8 and MSI (Multispectral Instrument) from Sentinel2. The analyzed images are from 2021, which were pre-processed, segmented and classified using the Random Forest algorithm. The analyzes showed that both classifications were qualified as "very good", obtaining Kappa Index (0.706) and Global Accuracy (76%) for the OLI images and Kappa Index (0.775) and Global Accuracy (84%) for the images MSI. The results obtained can support the planning and execution of new agricultural mapping and monitoring in the Cerrado, positively influencing the decision-making of research groups. [ABSTRACT FROM AUTHOR]

The growing application of artificial intelligence (AI) in complex real-world problems has shown an enormous potential to improve health services, including the role of pharmacists in the care process. Thus, the objective of this study was to identify, in the scientific literature, studies that addressed the use of machine learning (ML) algorithms applied to the activities of clinical pharmacists in patient care. This is an integrative review, conducted in the databases Pubmed, VHL Regional Portal, Cochrane Library and Embase. Original articles, related to the proposed topic, which were available and published before December 31, 2021, were included, without language limitations. There were 831 articles retrieved 5 of which were related to activities included in the pharmacotherapy review services (3) and therapeutic monitoring (2). Supervised (3) and unsupervised (2) ML techniques were used, with a variety of algorithms tested, with all studies published recently (2019-2021). It is concluded that the application of AI in clinical pharmacy is still discreet, signaling the challenges of the digital age. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ibero-Americana de Ciência da Informação is the property of Revista Ibero-Americana de Ciencia da Informacao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Texto Livre / Texto Livre: Linguagem e Tecnologia is the property of Revista Texto Livre: Linguagem e Tecnologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)