Search

Objective High-dimensional databases make it difficult to apply traditional learning algorithms to biomedical applications. Recent developments in computer technology have introduced deep learning (DL) as a potential solution to these difficulties. This study presents a novel intelligent decision support system based on a novel interpretation of data formalisation from tabular data in DL techniques. Once defined, it is used to diagnose the severity of obstructive sleep apnoea, distinguishing between moderate to severe and mild/no cases. Methods The study uses a complete database extract from electronic health records of 2472 patients, including anthropometric data, habits, medications, comorbidities, and patient-reported symptoms. The novelty of this methodology lies in the initial processing of the patients’ data, which is formalised into images. These images are then used as input to train a convolutional neural network (CNN), which acts as the inference engine of the system. Results The initial tests of the system were performed on a set of 247 samples from the Pulmonary Department of the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain), with an AUC value of ≈ 0.8. Conclusions This study demonstrates the benefits of an intelligent decision support system based on a novel data formalisation approach that allows the use of advanced DL techniques starting from tabular data. In this way, the ability of CNNs to recognise complex patterns using visual elements such as gradients and contrasts can be exploited. This approach effectively addresses the challenges of analysing large amounts of tabular data and reduces common problems such as bias and variance, resulting in improved diagnostic accuracy.

Scale-free brain activity, linked with learning, the integration of different time scales, and the formation of mental models, is correlated with a metastable cognitive basis. The spectral slope, a key aspect of scale-free dynamics, was proposed as a potential indicator to distinguish between different sleep stages. Studies suggest that brain networks maintain a consistent scale-free structure across wakefulness, anesthesia, and recovery. Although differences in anesthetic sensitivity between the sexes are recognized, these variations are not evident in clinical electroencephalographic recordings of the cortex. Recently, changes in the slope of the power law exponent of neural activity were found to correlate with changes in Rényi entropy, an extended concept of Shannon's information entropy. These findings establish quantifiers as a promising tool for the study of scale-free dynamics in the brain. Our study presents a novel visual representation called the Rényi entropy-complexity causality space, which encapsulates complexity, permutation entropy, and the Rényi parameter q. The main goal of this study is to define this space for classical dynamical systems within theoretical bounds. In addition, the study aims to investigate how well different time series mimicking scale-free activity can be discriminated. Finally, this tool is used to detect dynamic features in intracranial electroencephalography (iEEG) signals. To achieve these goals, the study implementse the Bandt and Pompe method for ordinal patterns. In this process, each signal is associated with a probability distribution, and the causal measures of Rényi entropy and complexity are computed based on the parameter q. This method is a valuable tool for analyzing simulated time series. It effectively distinguishes elements of correlated noise and provides a straightforward means of examining differences in behaviors, characteristics, and classifications. For the iEEG experimental data, the REM state showed a greater number of significant sex-based differences, while the supramarginal gyrus region showed the most variation across different modes and analyzes. Exploring scale-free brain activity with this framework could provide valuable insights into cognition and neurological disorders. The results may have implications for understanding differences in brain function between the sexes and their possible relevance to neurological disorders.

The use of digital technologies in teaching determines the development of new intellectual skills in students, which facilitates the application of investigation strategies, improves their capabilities in an integral way, helps them face the difficulties and demands of the current world and allows them to acquire more significant learning to have a good performance in the socio-educational environment. With a view to fulfilling the above, an institutional repository of open educational resources was developed for the Virtual Health University, based on the use of free and open-source technology tools that allow the standardization and interoperability among each of the repositories established in the National Health System, which will guarantee logic of academic organization.

Este estudio se enfoca en el papel de la tecnología en la gestión de las prácticas académicas, considerando las interacciones entre estudiantes, empresas/instituciones y la universidad, con el objetivo de proporcionar una solución tecnológica para abordar las dificultades que conlleva este proceso. Para ello, se utiliza una metodología mixta, que incluye un panel de expertos y una encuesta dirigida a estudiantes de prácticas en comunicación. Los resultados del análisis temático identifican cuatro bloques temáticos (Utilidad a lo largo del tiempo, Comunicación directa e inmediata, Contenido específico y utilidades prácticas, Agilidad y simplificación de procesos) y revelan un alto consenso entre los expertos y estudiantes respecto a la necesidad de una aplicación para la gestión de prácticas académicas. En base a estos análisis, se propone una aplicación que se estructura en torno a los bloques temáticos identificados, incluyendo funciones de registro, búsqueda, evaluación, comunicación y gestión de ofertas y currículos. En conclusión, la aplicación propuesta es una solución tecnológica eficaz para mejorar la interacción y la gestión de las prácticas académicas. Su implementación fortalecerá los procesos de enseñanza-aprendizaje al promover una comunicación fluida y simplificar la administración de las prácticas.

Long COVID is a condition that affects a significant proportion of patients who have had COVID-19. It is characterised by the persistence of associated symptoms after the acute phase of the illness has subsided. Although several studies have investigated the risk factors associated with long COVID, identifying which patients will experience long-term symptoms remains a complex task. Among the various symptoms, dyspnea is one of the most prominent due to its close association with the respiratory nature of COVID-19 and its disabling consequences. This work proposes a new intelligent clinical decision support system to predict dyspnea 12 months after a severe episode of COVID-19 based on the SeguiCovid database from the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain). The database is initially processed using a CART-type decision tree to identify the variables with the highest predictive power. Based on these variables, a cascade of expert systems has been defined with Mamdani-type fuzzy-inference engines. The rules for each system were generated using the Wang-Mendel automatic rule generation algorithm. At the output of the cascade, a risk indicator is obtained, which allows for the categorisation of patients into two groups: those with dyspnea and those without dyspnea at 12 months. This simplifies follow-up and the performance of studies aimed at those patients at risk. The system has produced satisfactory results in initial tests, supported by an AUC of 0.75, demonstrating the potential and usefulness of this tool in clinical practice.

Complementary feeding (CF) is defined as the feeding of infants that complements breastfeeding, or alternatively, feeding with a breast milk substitute, and is a process that is more than simply a guide as to what and how to introduce foods. The information provided by healthcare professionals must be up-to-date and evidence-based. Most of the recommendations that appear in the different international guidelines and position papers are widely applicable, but some must be regionalized or adapted to fit the conditions and reality of each geographic zone. The Nutrition Working Group of the Latin American Society for Pediatric Gastroenterology, Hepatology and Nutrition (LASPGHAN) summoned a group of experts from each of the society’s member countries, to develop a consensus on CF, incorporating, whenever possible, local information adapted to the reality of the region. The aim of the present document is to show the results of that endeavor. Utilizing the Delphi method, a total of 34 statements on relevant aspects of CF were evaluated, discussed, and voted upon. Resumen: La alimentación complementaria (AC) se define como la alimentación de los lactantes que complementa a la lactancia humana o en su defecto, a la lactancia con un sucedáneo de la leche humana, y es un proceso que va más allá de simplemente una guía sobre qué y cómo introducir los alimentos. La información brindada por parte de los profesionales de la salud debe ser actualizada y basada en evidencia. Existen diferentes guías o documentos de posición a nivel internacional, que, aunque la mayoría de las recomendaciones pueden ser aplicables, hay algunas otras que requieren una regionalización o adecuación a las condiciones y realidad de cada zona. El grupo de trabajo de Nutrición de la Sociedad Latinoamericana de Gastroenterología, Hepatología y Nutrición Pediátrica convocó a un grupo de expertos, representantes de cada uno de los países que conforman la sociedad, con el objetivo de desarrollar un consenso sobre AC, que incorporó cuando así fue posible, información local que se adapte a la realidad de la región. El objetivo de este documento es mostrar los resultados de dicho trabajo. A través de metodología Delphi, se evaluaron, discutieron y votaron un total de 34 declaraciones o enunciados con respecto a aspectos relevantes de la AC.

Resumen: La alimentación complementaria (AC) se define como la alimentación de los lactantes que complementa a la lactancia materna o en su defecto, a la lactancia con un sucedáneo de la leche materna, y es un proceso que va más allá de simplemente una guía sobre qué y cómo introducir los alimentos. La información brindada por parte de los profesionales de la salud debe ser actualizada y basada en evidencia. Existen diferentes guías o documentos de posición a nivel internacional, que, aunque la mayoría de las recomendaciones pueden ser aplicables, hay algunas otras que requieren una regionalización o adecuación a las condiciones y realidad de cada zona. El grupo de trabajo de Nutrición de la Sociedad Latinoamericana de Gastroenterología, Hepatología y Nutrición Pediátrica convocó a un grupo de expertos, representantes de cada uno de los países que conforman la sociedad, con el objetivo de desarrollar un consenso sobre la AC, que incorporó cuando así fue posible, información local que se adapte a la realidad de la región. El objetivo de este documento es mostrar los resultados de dicho trabajo. A través de metodología Delphi, se evaluaron, discutieron y votaron un total de 34 declaraciones o enunciados con respecto a aspectos relevantes de la AC. Abstract: Complementary feeding (CF) is defined as the feeding of infants that complements breastfeeding, or alternatively, feeding with a breast milk substitute, and is a process that is more than simply a guide as to what and how to introduce foods. The information provided by healthcare professionals must be up-to-date and evidence-based. Most of the recommendations that appear in the different international guidelines and position papers are widely applicable, but some must be regionalized or adapted to fit the conditions and reality of each geographic zone. The Nutrition Working Group of the Latin American Society for Pediatric Gastroenterology, Hepatology and Nutrition (LASPGHAN) summoned a group of experts from each of the society's member countries, to develop a consensus on CF, incorporating, whenever possible, local information adapted to the reality of the region. The aim of the present document is to show the results of that endeavor. Utilizing the Delphi method, a total of 34 statements on relevant aspects of CF were evaluated, discussed, and voted upon.

The advances in technologies of information and communication have facilitated to trace strategies for the reorganization of the teaching-educational process in these times of sanitary crisis due to the pandemic of COVID-19 that has limited the presence in classrooms. In the University of Medical Sciences in Santiago de Cuba, the Health Virtual University integrates several platforms for the predegree and postdegree teaching-learning, such as classroom and virtual clinic, repository, courses and humanities directory; however, an insufficient use of these potentialities is observed. For such a reason, in this work the necessity of increasing the use of the integrative portal for information management in Health Virtual University is emphasized.

This research focuses on the study of the virtual community of fans of the procedural video game No Man's Sky and how the contributions of the wiki fandom are projected in the official updates published until February 2024. Using a scientific methodology based on the content analysis of user interactions, we propose an exploratory study consisting of a virtual ethnography that identifies the audio content generated that has led to changes in the production of the updates of the video game analysed, thus establishing an effective correlation with the technological evolution of the title in question. [ABSTRACT FROM AUTHOR]

En las últimas décadas se han llevado a cabo diferentes estudios que profundizan en las condiciones que explican el compromiso de los individuos con las tareas académicas. Estas investigaciones evidencian la necesidad de combinar factores cognitivos —las capacidades, conocimientos o estrategias— con aspectos de carácter motivacional, como la disposición, la intención o las creencias autorreferidas. En este contexto se sitúa este estudio, que tiene por objeto identificar perfiles motivacionales, en función de la autoeficacia y las metas académicas, y estudiar su relación con el compromiso (esfuerzo y persistencia en la tarea), las emociones (miedo al fracaso y afecto positivo) y con el rendimiento académico (matemáticas y ciencias). La muestra está formada por 7,524 estudiantes (50.9% mujeres) de Educación Secundaria que participaron en la evaluación PISA 2018 (M = 15.84, DT = 0.29). Empleando el Latent Profile Analysis se identificaron cinco perfiles motivacionales. Los perfiles con puntuaciones más adaptativas en autoeficacia y metas de aprendizaje reconocían menor miedo al fracaso, más afecto positivo, esfuerzo/persistencia y obtenían mejor rendimiento académico (científico y matemático). Discutidos a la luz de la teoría motivacional, los resultados de esta investigación evidencian el papel de la orientación a metas y las creencias de autoeficacia en la promoción tanto del compromiso y rendimiento académico como del bienestar emocional del estudiante.

This work analyzes the reliability of the radiochemical purity control method of 99mTc-MIBI used in nuclear medicine services and the influence of the greater presence of radiochemical impurities in the diagnostic quality of the obtained images.

Obstructive sleep apnea (OSA), characterized by recurrent episodes of partial or total obstruction of the upper airway during sleep, is currently one of the respiratory pathologies with the highest incidence worldwide. This situation has led to an increase in the demand for medical appointments and specific diagnostic studies, resulting in long waiting lists, with all the health consequences that this entails for the affected patients. In this context, this paper proposes the design and development of a novel intelligent decision support system applied to the diagnosis of OSA, aiming to identify patients suspected of suffering from the pathology. For this purpose, two sets of heterogeneous information are considered. The first one includes objective data related to the patient’s health profile, with information usually available in electronic health records (anthropometric information, habits, diagnosed conditions and prescribed treatments). The second type includes subjective data related to the specific OSA symptomatology reported by the patient in a specific interview. For the processing of this information, a machine-learning classification algorithm and a set of fuzzy expert systems arranged in cascade are used, obtaining, as a result, two indicators related to the risk of suffering from the disease. Subsequently, by interpreting both risk indicators, it will be possible to determine the severity of the patients’ condition and to generate alerts. For the initial tests, a software artifact was built using a dataset with 4400 patients from the Álvaro Cunqueiro Hospital (Vigo, Galicia, Spain). The preliminary results obtained are promising and demonstrate the potential usefulness of this type of tool in the diagnosis of OSA.

The use of intelligent systems in clinical diagnostics has evolved, integrating statistical learning and knowledge-based representation models. Two recent works propose the identification of risk factors for the diagnosis of obstructive sleep apnea (OSA). The first uses statistical learning to identify indicators associated with different levels of the apnea-hypopnea index (AHI). The second paper combines statistical and symbolic inference approaches to obtain risk indicators (Statistical Risk and Symbolic Risk) for a given AHI level. Based on this, in this paper we propose a new intelligent system that considers different AHI levels and generates risk pairs for each level. A learning-based model generates Statistical Risks based on objective patient data, while a cascade of fuzzy expert systems determines a Symbolic Risk using symptom data from patient interviews. The aggregation of risk pairs at each level involves a fuzzy expert system with automatically generated fuzzy rules using the Wang-Mendel algorithm. This aggregation produces an Apnea Risk indicator for each AHI level, allowing discrimination between OSA and non-OSA cases, along with appropriate recommendations. This approach improves variability, usefulness, and interpretability, increasing the reliability of the system. Initial tests on data from 4400 patients yielded AUC values of 0.74–0.88, demonstrating the potential benefits of the proposed intelligent system architecture.

In this work 99mTc-MIBI cardiac scintigraphs are quantitively analyzed with the aim to determine the radiopharmaceutical uptake in the heart (organ of interest) and in the liver. The impact of the radioactivity distribution in the diagnostic quality of the images is discussed.

Scale-free brain activity, linked with learning, the integration of different time scales, and the formation of mental models, is correlated with a metastable cognitive basis. The spectral slope, a key aspect of scale-free dynamics, was proposed as a potential indicator to distinguish between different sleep stages. Studies suggest that brain networks maintain a consistent scale-free structure across wakefulness, anesthesia, and recovery. Although differences in anesthetic sensitivity between the sexes are recognized, these variations are not evident in clinical electroencephalographic recordings of the cortex. Recently, changes in the slope of the power law exponent of neural activity were found to correlate with changes in Rényi entropy, an extended concept of Shannon's information entropy. These findings establish quantifiers as a promising tool for the study of scale-free dynamics in the brain. Our study presents a novel visual representation called the Rényi entropy-complexity causality space, which encapsulates complexity, permutation entropy, and the Rényi parameter q. The main goal of this study is to define this space for classical dynamical systems within theoretical bounds. In addition, the study aims to investigate how well different time series mimicking scale-free activity can be discriminated. Finally, this tool is used to detect dynamic features in intracranial electroencephalography (iEEG) signals. To achieve these goals, the study implementse the Bandt and Pompe method for ordinal patterns. In this process, each signal is associated with a probability distribution, and the causal measures of Rényi entropy and complexity are computed based on the parameter q. This method is a valuable tool for analyzing simulated time series. It effectively distinguishes elements of correlated noise and provides a straightforwardmeans of examining differences in behaviors, characteristics, and classifications. For the iEEG experimental data, the REM state showed a greater number of significant sex-based differences, while the supramarginal gyrus region showed themost variation across differentmodes and analyzes. Exploring scale-free brain activity with this framework could provide valuable insights into cognition and neurological disorders. The results may have implications for understanding differences in brain function between the sexes and their possible relevance to neurological disorders. [ABSTRACT FROM AUTHOR]

The aim of the present study was to predict future changes in biodiversity attributes (richness, rarity, heterogeneity, evenness, functional diversity and taxonomic diversity) of freshwater fish species in river basins around the world, under different climate scenarios. To do this, we use a new methodological approach implemented within the ModestR software (NOO3D) which allows estimating simple species distribution predictions for future climatic scenarios. Data from 16,825 freshwater fish species were used, representing a total of 1,464,232 occurrence records. WorldClim 1.4 variables representing average climate variables for the 1960–1990 period, together with elevation measurements, were used as predictors in these distribution models, as well as in the selection of the most important variables that account for species distribution changes in two scenarios (Representative Concentration Pathways 4.5 and 6.0). The predictions produced suggest the extinction of almost half of current freshwater fish species in the coming decades, with a pronounced decline in tropical regions and a greater extinction likelihood for species with smaller body size and/or limited geographical ranges.

Forest fires have become a major problem that every year has devastating consequences at the environmental level, negatively impacting the social and economic spheres of the affected regions. Aiming to mitigate these terrible effects, intelligent prediction models focused on early fire detection are becoming common practice. Considering mainly a preventive approach, these models often use tools that indifferently apply statistical or symbolic inference techniques. However, exploring the potential for the hybrid use of both, as is already being done in other research areas, is a significant novelty with direct application to early fire detection. In this line, this work proposes the design, development, and proof of concept of a new intelligent hybrid system that aims to provide support to the decisions of the teams responsible for defining strategies for the prevention, detection, and extinction of forest fires. The system determines three risk levels: a general one called Objective Technical Fire Risk, based on machine learning algorithms, which determines the global danger of a fire in some area of the region under study, and two more specific others which indicate the risk over a limited area of the region. These last two risk levels, expressed in matrix form and called Technical Risk Matrix and Expert Risk Matrix, are calculated through a convolutional neural network and an expert system, respectively. After that, they are combined by means of another expert system to determine the Global Risk Matrix that quantifies the risk of fire in each of the study regions and generates a visual representation of these results through a color map of the region itself. The proof of concept of the system has been carried out on a set of historical data from fires that occurred in the Montesinho Natural Park (Portugal), demonstrating its potential utility as a tool for the prevention and early detection of forest fires. The intelligent hybrid system designed has demonstrated excellent predictive capabilities in such a complex environment as forest fires, which are conditioned by multiple factors. Future improvements associated with data integration and the formalization of knowledge bases will make it possible to obtain a standard tool that could be used and validated in real time in different forest areas.

Healthcare-Associated Infections (HAIs) are an important public health problem that impacts negatively on hospital costs and patient prognosis. Given the importance of the hospital environment in the development of HAIs, the objective was to evaluate the bacterial profile on surfaces and equipment of the Orthopedic Clinic of the Hospital Universitário do Vale do São Francisco. Methods: This is a cross-sectional, descriptive, quantitative study. Samples were collected in 13 wards, each ward with four beds and one was chosen at random, where surfaces and equipment were sampled using swabs soaked in saline and a 1cm2 filter paper mold to standardize the samples. After passing the swab, they were stored in a tube containing 5mL of BHI (Brain Heart Infusion) liquid medium. Then, samples were transported to the Clinical Analysis Laboratory/Microbiology Sector where the microbiological analyzes were performed. Results: In total, 257 bacteria were observed, of which 5.11% were possible causes of hospital infection and 79% coagulase-negative Staphylococcus. Antibiograms of these were performed and different resistance profiles were found. The bathroom doorknob, a high-touch surface, presented the greatest variety of species among the evaluated surfaces. Conclusion: Surfaces and equipment of the evaluated clinic present possible bacteria that cause hospital infection with different profiles of antimicrobial resistance, contributing to possible cross infections

The triage processes prior to the assignation of healthcare resources in hospitals are some of the decision-making processes that more severely affect patients. This effect gets even worse in health emergency situations and intensive care units (ICUs). Aiming to facilitate the decision-making process, in this work the use of vague fuzzy numbers is proposed, aiming to define a multi-attribute patient hierarchization method to be used in emergency situations at hospital ICUs. The incorporation of fuzzy models allows for modelling the vagueness and uncertainty associated with decision criteria evaluation, with which more efficient support is provided to the decision-making process. After defining the methodology, the effectiveness of this new system for patient hierarchization is shown in a case study. As a consequence of that, it is proved that the integration of decision-support systems into healthcare environments results to be efficient and productive, suggesting that if a part of the decision process is supported by these systems, then the errors associated with wrong interpretations and/or diagnoses might be reduced.