Interrelationships among skeletal age, growth status and motor performances in female athletes 10-15 years.

BACKGROUND: Motor performances of youth are related to growth and maturity status, among other factors. AIM: To estimate the contribution of skeletal maturity status per se to the motor performances of female athletes aged 10-15 years and the mediation effects of growth status on the relationships. SUBJECTS AND METHODS: Skeletal age (TW3 RUS SA), body size, proportions, estimated fat-free mass (FFM), motor performances, training history and participation motivation were assessed in 80 non-skeletally mature female participants in several sports. Hierarchical and regression-based statistical mediation analyses were used. RESULTS: SA per se explained a maximum of 1.8% and 5.8% of the variance in motor performances of athletes aged 10-12 and 13-15 years, respectively, over and above that explained by covariates. Body size, proportions, and hours per week of training and participation motivation explained, respectively, a maximum of 40.7%, 18.8%, and 22.6% of the variance in performances. Mediation analysis indicated specific indirect effects of SA through stature and body mass, alone or in conjunction with FFM on performances. CONCLUSION: SA per se accounted for small and non-significant amounts of variance in several motor performances of female youth athletes; rather, SA influenced performances indirectly through effects on stature, body mass and estimated FFM.

Genetic regulation of body size and morphology in children: a twin study of 22 anthropometric traits.

BACKGROUND: Anthropometric measures show high heritability, and genetic correlations have been found between obesity-related traits. However, we lack a comprehensive analysis of the genetic background of human body morphology using detailed anthropometric measures. METHODS: Height, weight, 7 skinfold thicknesses, 7 body circumferences and 4 body diameters (skeletal breaths) were measured in 214 pairs of twin children aged 3-18 years (87 monozygotic pairs) in the Autonomous Region of Madeira, Portugal. Factor analysis (Varimax rotation) was used to analyze the underlying structure of body physique. Genetic twin modeling was used to estimate genetic and environmental contributions to the variation and co-variation of the anthropometric traits. RESULTS: Together, two factors explained 80% of the variation of all 22 anthropometric traits in boys and 73% in girls. Obesity measures (body mass index, skinfold thickness measures, as well as waist and hip circumferences) and limb circumferences loaded most strongly on the first factor, whereas height and body diameters loaded especially on the second factor. These factors as well as all anthropometric measures showed high heritability (80% or more for most of the traits), whereas the rest of the variation was explained by environmental factors not shared by co-twins. Obesity measures showed high genetic correlations (0.75-0.98). Height showed the highest genetic correlations with body diameter measures (0.58-0.76). Correlations between environmental factors not shared by co-twins were weaker than the genetic correlations but still substantial. The correlation patterns were roughly similar in boys and girls. CONCLUSIONS: Our results show high genetic correlations underlying the human body physique, suggesting that there are sets of genes widely affecting anthropometric traits. Better knowledge of these genetic variants can help to understand the development of obesity and other features of the human physique.

Connected Mental Health Solutions: Global Attitudes, Preferences, and Concerns.

Background: Connected mental health (CMH) presents several technology-based solutions, which can help overcome many mental care delivery barriers. However, attitudes toward the use of CMH are diverse and differ from a cohort to another. Objective: The purpose of this study is to investigate the global attitudes toward CMH use and assess the use of technology for mental care. Methods: This study presents a synthesis of literature available in Scopus, Science Direct, and PubMed digital libraries, investigating attitudes toward CMH in different cohorts from different countries, based on a systematic review of relevant publications. This study also analyzes technology use patterns of the cohorts investigated, the reported preferred criteria that should be considered in CMH, and issues and concerns regarding CMH use. Results: One hundred and one publications were selected and analyzed. These publications were originated from different countries, with the majority (n = 23) being conducted in Australia. These studies reported positive attitudes of investigated cohorts toward CMH use and high technology use and ownership. Several preferred criteria were reported, mainly revolving around providing blended care functionalities, educational content, and mental health professionals (MHPs) support. Whereas concerns and issues related to CMH use addressed technical problems related to access to technology and to CMH solutions, the digital divide, lack of knowledge and use of CMH, and general reservations to use CMH. Concerns related to institutional and work barriers were also identified. Conclusions: Attitudes toward CMH show promising results from users and MHP views. However, factors such as providing blended care options and considering technical concerns should be taken into consideration for the successful adoption of CMH.

ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment.

Air quality levels do not just affect climate change; rather, it leaves a significant impact on public health and wellbeing. Indoor air pollution is the major contributor to increased mortality and morbidity rates. This paper is focused on the assessment of indoor air quality based on several important pollutants (PM10, PM2.5, CO2, CO, tVOC, and NO2). These pollutants are responsible for potential health issues, including respiratory disease, central nervous system dysfunction, cardiovascular disease, and cancer. The pollutant concentrations were measured from a rural site in India using an Internet of Things-based sensor system. An Adaptive Dynamic Fuzzy Inference System Tree was implemented to process the field variables. The knowledge base for the proposed model was designed using a global optimization algorithm. However, the model was tuned using a local search algorithm to achieve enhanced prediction performance. The proposed model gives normalized root mean square error of 0.6679, 0.6218, 0.1077, 0.2585, 0.0667 and 0.0635 for PM10, PM2.5, CO2, CO, tVOC, and NO2, respectively. This approach was compared with the existing studies in the literature, and the approach was also validated against the online benchmark dataset.

Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia.

New computational methods have emerged through science and technology to support the diagnosis of mental health disorders. Predictive models developed from machine learning algorithms can identify disorders such as schizophrenia and support clinical decision making. This research aims to compare the performance of machine learning algorithms: Decision Tree, AdaBoost, Random Forest, Naïve Bayes, Support Vector Machine, and k-Nearest Neighbor in the prediction of hospitalized patients with schizophrenia. The data set used in the study contains a total of 11,884 electronic admission records corresponding to 6933 patients with various mental health disorders; these records belong to the acute units of 11 public hospitals in a region of Spain. Of the total, 5968 records correspond to patients diagnosed with schizophrenia (3002 patients) and 5916 records correspond to patients with other mental health disorders (3931 patients). The results recommend Random Forest with the best accuracy of 72.7%. Furthermore, this algorithm presents 79.6%, 72.8%, 72.7%, and 72.7% for AUC, precision, F1-Score, and recall, respectively. The results obtained suggest that the use of machine learning algorithms can classify hospitalized patients with schizophrenia in this population and help in the hospital management of this type of disorder, to reduce the costs associated with hospitalization.

Occupational Stress Monitoring Using Biomarkers and Smartwatches: A Systematic Review.

This article presents a systematic review of the literature concerning scientific publications on wrist wearables that can help to identify stress levels. The study is part of a research project aimed at modeling a stress surveillance system and providing coping recommendations. The investigation followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. In total, 38 articles were selected for full reading, and 10 articles were selected owing to their alignment with the study proposal. The types of technologies used in the research stand out amongst our main results after analyzing the articles. It is noteworthy that stress assessments are still based on standardized questionnaires, completed by the participants. The main biomarkers collected by the devices used in the selected works included: heart rate variation, cortisol analysis, skin conductance, body temperature, and blood volume at the wrist. This study concludes that developing a wrist wearable for stress identification using physiological and chemical sensors is challenging but possible and applicable.

Chest X-ray analysis empowered with deep learning: A systematic review.

Chest radiographs are widely used in the medical domain and at present, chest X-radiation particularly plays an important role in the diagnosis of medical conditions such as pneumonia and COVID-19 disease. The recent developments of deep learning techniques led to a promising performance in medical image classification and prediction tasks. With the availability of chest X-ray datasets and emerging trends in data engineering techniques, there is a growth in recent related publications. Recently, there have been only a few survey papers that addressed chest X-ray classification using deep learning techniques. However, they lack the analysis of the trends of recent studies. This systematic review paper explores and provides a comprehensive analysis of the related studies that have used deep learning techniques to analyze chest X-ray images. We present the state-of-the-art deep learning based pneumonia and COVID-19 detection solutions, trends in recent studies, publicly available datasets, guidance to follow a deep learning process, challenges and potential future research directions in this domain. The discoveries and the conclusions of the reviewed work have been organized in a way that researchers and developers working in the same domain can use this work to support them in taking decisions on their research.

Environment and physiology shape Arctic ungulate population dynamics.

Species conservation in a rapidly changing world requires an improved understanding of how individuals and populations respond to changes in their environment across temporal scales. Increased warming in the Arctic puts this region at particular risk for rapid environmental change, with potentially devastating impacts on resident populations. Here, we make use of a parameterized full life cycle, individual-based energy budget model for wild muskoxen, coupling year-round environmental data with detailed ontogenic metabolic physiology. We show how winter food accessibility, summer food availability, and density dependence drive seasonal dynamics of energy storage and thus life history and population dynamics. Winter forage accessibility defined by snow depth, more than summer forage availability, was the primary determinant of muskox population dynamics through impacts on calf recruitment and longer term carryover effects of maternal investment. Simulations of various seasonal snow depth and plant biomass and quality profiles revealed that timing of and improved/limited winter forage accessibility had marked influence on calf recruitment (±10-80%). Impacts on recruitment were the cumulative result of condition-driven reproductive performance at multiple time points across the reproductive period (ovulation to calf weaning) as a trade-off between survival and reproduction. Seasonal and generational condition effects of snow-rich winters interacted with age structure and density to cause pronounced long-term consequences on population growth and structure, with predicted population recovery times from even moderate disturbances of 10 years or more. Our results show how alteration in winter forage accessibility, mediated by snow depth, impacts the dynamics of northern herbivore populations. Further, we present here a mechanistic and state-based model framework to assess future scenarios of environmental change, such as increased or decreased snowfall or plant biomass and quality to impact winter and summer forage availability across the Arctic.

Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review.

Alzheimer's disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability.

Radiological Scouting, Monitoring and Inspection Using Drones.

Human populations and natural ecosystems are bound to be exposed to ionizing radiation from the deposition of artificial radionuclides resulting from nuclear accidents, nuclear devices or radiological dispersive devices ("dirty bombs"). On the other hand, Naturally Occurring Radioactive Material industries such as phosphate production or uranium mining, contribute to the on site storage of residuals with enhanced concentrations of natural radionuclides. Therefore, in the context of the European agreements concerning nuclear energy, namely the European Atomic Energy Community Treaty, monitoring is an essential feature of the environmental radiological surveillance. In this work, we obtain 3D maps from outdoor scenarios, and complete such maps with measured radiation levels and with its radionuclide signature. In such scenarios, we face challenges such as unknown and rough terrain, limited number of sampled locations and the need for different sensors and therefore different tasks. We propose a radiological solution for scouting, monitoring and inspecting an area of interest, using a fleet of drones and a controlling ground station. First, we scout an area with a Light Detection and Ranging sensor onboard a drone to accurately 3D-map the area. Then, we monitor that area with a Geiger-Müller Counter at a low-vertical distance from the ground to produce a radiological (heat)map that is overlaid on the 3D map of the scenario. Next, we identify the hotspots of radiation, and inspect them in detail using a drone by landing on them, to reveal its radionuclide signature using a Cadmium-Zinc-Telluride detector. We present the algorithms used to implement such tasks both at the ground station and on the drones. The three mission phases were validated using actual experiments in three different outdoor scenarios. We conclude that drones can not only perform the mission efficiently, but in general they are faster and as reliable as personnel on the ground.

A Systematic Literature Review on e-Mental Health Solutions to Assist Health Care Workers During COVID-19.

Background: e-Mental health is an established field of exploiting information and communication technologies for mental health care. It offers different solutions and has shown effectiveness in managing many psychological issues. Introduction: The coronavirus disease 2019 (COVID-19) pandemic has critically influenced health care systems and health care workers (HCWs). HCWs are working under hard conditions, and are suffering from different psychological issues, including anxiety, stress, and depression. Consequently, there is an undeniable need of mental care interventions for HCWs. Under the circumstances caused by COVID-19, e-health interventions can be used as tools to assist HCWs with their mental health. These solutions can provide mental health care support remotely, respecting the recommended safety measures. Materials and Methods: This study aims to identify e-mental health interventions, reported in the literature, that are developed for HCWs during the COVID-19 pandemic. A systematic literature review was conducted following the PRISMA protocol by searching the following digital libraries: IEEE, ACM, ScienceDirect, Scopus, and PubMed. Results and Discussion: Eleven publications were selected. The identified e-mental health interventions consisted of social media platforms, e-learning content, online resources and mobile applications. Only 27% of the studies included empirical evaluation of the reported interventions, 55% listed challenges and limitations related to the adoption of the reported interventions. And 45% presented interventions developed specifically for HCWs in China. The overall feedback on the identified interventions was positive, yet a lack of empirical evaluation was identified, especially regarding qualitative evidence. Conclusions: The COVID-19 pandemic has highlighted the importance and need for e-mental health solutions for HCWs.

Machine Learning in Medical Emergencies: a Systematic Review and Analysis.

Despite the increasing demand for artificial intelligence research in medicine, the functionalities of his methods in health emergency remain unclear. Therefore, the authors have conducted this systematic review and a global overview study which aims to identify, analyse, and evaluate the research available on different platforms, and its implementations in healthcare emergencies. The methodology applied for the identification and selection of the scientific studies and the different applications consist of two methods. On the one hand, the PRISMA methodology was carried out in Google Scholar, IEEE Xplore, PubMed ScienceDirect, and Scopus. On the other hand, a review of commercial applications found in the best-known commercial platforms (Android and iOS). A total of 20 studies were included in this review. Most of the included studies were of clinical decisions (n = 4, 20%) or medical services or emergency services (n = 4, 20%). Only 2 were focused on m-health (n = 2, 10%). On the other hand, 12 apps were chosen for full testing on different devices. These apps dealt with pre-hospital medical care (n = 3, 25%) or clinical decision support (n = 3, 25%). In total, half of these apps are based on machine learning based on natural language processing. Machine learning is increasingly applicable to healthcare and offers solutions to improve the efficiency and quality of healthcare. With the emergence of mobile health devices and applications that can use data and assess a patient's real-time health, machine learning is a growing trend in the healthcare industry.

Sensors for indoor air quality monitoring and assessment through Internet of Things: a systematic review.

The growing populations around the world are closely associated with rising levels of air pollution. The impact is not restricted to outdoor areas. Moreover, the health of building occupants is also deteriorating due to poor indoor air quality. As per the World Health Organization, indoor air pollution is a leading cause of 1.6 million premature deaths annually. Therefore, numerous companies have started the development of low-cost sensors to monitor indoor air pollution with the Internet of Things-based applications. However, due to the close association of air pollution levels to the mortality and morbidity rates, communities face several limitations while selecting sensors to address this public health challenge. The main contribution of this systematic review is to present a qualitative and quantitative evaluation of low-cost sensors while providing deep insights into the selection criteria for adequate monitoring. The authors in this paper discussed studies published after the year 2015, and it includes an analysis of papers published in the English language only. Moreover, this study highlights crucial research questions, states answers, and provides recommendations for future research studies. The outcomes of this paper will be useful for students, researchers, and industry members concerning the upcoming research and manufacturing activities. The results show that 28 studies (70%) include indoor thermal comfort assessment, 26 (65%) and 12 (30%) studies include CO2 and CO sensors, respectively. In total, 32 (45.7%) out of 71 sensors (whose prices are available) discussed in this study are available in a price below the US $20 over online marketplaces. Furthermore, the authors conclude that 77.5% of the analyzed literature does not include calibration details, and the accuracy specification is missing for 39.4% sensors.

Internet of Things and Enhanced Living Environments: Measuring and Mapping Air Quality Using Cyber-physical Systems and Mobile Computing Technologies.

This paper presents a real-time air quality monitoring system based on Internet of Things. Air quality is particularly relevant for enhanced living environments and well-being. The Environmental Protection Agency and the World Health Organization have acknowledged the material impact of air quality on public health and defined standards and policies to regulate and improve air quality. However, there is a significant need for cost-effective methods to monitor and control air quality which provide modularity, scalability, portability, easy installation and configuration features, and mobile computing technologies integration. The proposed method allows the measuring and mapping of air quality levels considering the spatial-temporal information. This system incorporates a cyber-physical system for data collection and mobile computing software for data consulting. Moreover, this method provides a cost-effective and efficient solution for air quality supervision and can be installed in vehicles to monitor air quality while travelling. The results obtained confirm the implementation of the system and present a relevant contribution to enhanced living environments in smart cities. This supervision solution provides real-time identification of unhealthy behaviours and supports the planning of possible interventions to increase air quality.

MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management.

In healthcare applications, numerous sensors and devices produce massive amounts of data which are the focus of critical tasks. Their management at the edge of the network can be done by Fog computing implementation. However, Fog Nodes suffer from lake of resources That could limit the time needed for final outcome/analytics. Fog Nodes could perform just a small number of tasks. A difficult decision concerns which tasks will perform locally by Fog Nodes. Each node should select such tasks carefully based on the current contextual information, for example, tasks' priority, resource load, and resource availability. We suggest in this paper a Multi-Agent Fog Computing model for healthcare critical tasks management. The main role of the multi-agent system is mapping between three decision tables to optimize scheduling the critical tasks by assigning tasks with their priority, load in the network, and network resource availability. The first step is to decide whether a critical task can be processed locally; otherwise, the second step involves the sophisticated selection of the most suitable neighbor Fog Node to allocate it. If no Fog Node is capable of processing the task throughout the network, it is then sent to the Cloud facing the highest latency. We test the proposed scheme thoroughly, demonstrating its applicability and optimality at the edge of the network using iFogSim simulator and UTeM clinic data.

Particulate Matter Monitoring and Assessment through Internet of Things: a Health Information System for Enhanced Living Environments.

People spend most of their time inside buildings. Therefore, indoor air quality monitoring contributes to improve health and well-being. Several studies focus on the critical impact of particulate matter on residential air quality. In 2016, particulate matter caused 412 thousand premature deaths in 41 European countries. This paper presents the development of an affordable health information system for enhanced living environments. The authors propose a cost-effective, modular, scalable, and easy installation solution for particulate matter monitoring. The system is connected to ThingSpeak. It can be installed in any type of building. It requires only a power source and a Wi-Fi network with internet access. The main contribution of this paper is to present the detailed architecture and testing results. The particulate matter monitoring system was installed for one week in a domestic kitchen with an open fireplace. The results showed impact of the biomass burning on indoor air quality. The mean values per day ranged from: 10.53 to 50.62 µg/m3 for PM1.0, 15.35 to 69.37 µg/m3 for PM2.5, and 20.1 to 90.69 µg/m3 for PM10. The maximum values per hour were registered at 13:00: 72.14 µg/m3 for PM1.0, 99.70 µg/m3 for PM2.5, and 132.13 µg/m3 for PM10. Cost-effective sensors do not have the accuracy level of industrial equipment. Therefore, they should not be used for numerical and in-depth accurate characterization of the environment. Nevertheless, continuous particulate matter monitoring provides consistent data series for analysis of indoor air quality evolution.

Predicting Absenteeism and Temporary Disability Using Machine Learning: a Systematic Review and Analysis.

The main objective of this paper is to present a systematic analysis and review of the state of the art regarding the prediction of absenteeism and temporary incapacity using machine learning techniques. Moreover, the main contribution of this research is to reveal the most successful prediction models available in the literature. A systematic review of research papers published from 2010 to the present, related to the prediction of temporary disability and absenteeism in available in different research databases, is presented in this paper. The review focuses primarily on scientific databases such as Google Scholar, Science Direct, IEEE Xplore, Web of Science, and ResearchGate. A total of 58 articles were obtained from which, after removing duplicates and applying the search criteria, 18 have been included in the review. In total, 44% of the articles were published in 2019, representing a significant growth in scientific work regarding these indicators. This study also evidenced the interest of several countries. In addition, 56% of the articles were found to base their study on regression methods, 33% in classification, and 11% in grouping. After this systematic review, the efficiency and usefulness of artificial neural networks in predicting absenteeism and temporary incapacity are demonstrated. The studies regarding absenteeism and temporary disability at work are mainly conducted in Brazil and India, which are responsible for 44% of the analyzed papers followed by Saudi Arabia, and Australia which represented 22%. ANNs are the most used method in both classification and regression models representing 83% and 80% of the analyzed works, respectively. Only 10% of the literature use SVM, which is the less used method in regression models. Moreover, Naïve Bayes is the less used method in classification models representing 17%.

An Experience of Electronic Health Records Implementation in a Mexican Region.

Employing software engineering to build an integrated, standardized, and scalable solution is closely associated with the healthcare domain. Furthermore, new diagnostic techniques have been developed to obtain better results in less time, saving costs, and bringing services closer to the most unprotected areas. This paper presents the integration of a top-notch component, such as hardware, software, telecommunications, and medical equipment, to produce a complete system of Electronic Health Record (EHR). The EHR implementation aims to contribute to the expansion of the health services offer concerning people who live in locations where typically have difficult access to medical care. The methodology throughout the work is a Strategic Planning to set priorities, focus energy and resources, strengthen operations, ensure that directors, managers, employees, and other stakeholders are working toward common goals, establish agreement around intended outcomes/results. A medical and technical team is incorporated to complete the tasks of process and requirements analysis, software coding and design, technical support, training, and coaching for EHR system users throughout the implementation process. The adoption of those tools reflect notably some expected results and benefits on patient care. The EHR implementation ensures that information collection does not duplicate already existing information or duplicate effort and maximize the practical use of the data collected. Moreover, the EHR reduces mistakes in hospital readmissions, improves paperwork, promotes the progress of the state's health care system providing emergency, specialty, and primary health care in a rural area of Campeche. The EHR implementation is critical to support decision making and to promote public health. The total number of consults increased markedly from 2012 (14021) to 2019 (34751). The most commonly treated diseases in this region of Mexico are hypertension (17632) and diabetes (13156). The best results are obtained in the Nutrition (20,61%) and clinical psychology services (16,67%), and the worst levels are registered in pediatric and surgical oncology services where only 1,59% and 1,97% of the patients are admitted in less than 30 min, respectively.

Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review.

According to the World Health Organization (WHO) report in 2016, around 800,000 of individuals have committed suicide. Moreover, suicide is the second cause of unnatural death in people between 15 and 29 years. This paper reviews state of the art on the literature concerning the use of machine learning methods for suicide detection on social networks. Consequently, the objectives, data collection techniques, development process and the validation metrics used for suicide detection on social networks are analyzed. The authors conducted a scoping review using the methodology proposed by Arksey and O'Malley et al. and the PRISMA protocol was adopted to select the relevant studies. This scoping review aims to identify the machine learning techniques used to predict suicide risk based on information posted on social networks. The databases used are PubMed, Science Direct, IEEE Xplore and Web of Science. In total, 50% of the included studies (8/16) report explicitly the use of data mining techniques for feature extraction, feature detection or entity identification. The most commonly reported method was the Linguistic Inquiry and Word Count (4/8, 50%), followed by Latent Dirichlet Analysis, Latent Semantic Analysis, and Word2vec (2/8, 25%). Non-negative Matrix Factorization and Principal Component Analysis were used only in one of the included studies (12.5%). In total, 3 out of 8 research papers (37.5%) combined more than one of those techniques. Supported Vector Machine was implemented in 10 out of the 16 included studies (62.5%). Finally, 75% of the analyzed studies implement machine learning-based models using Python.

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network.

COVID-19 infection was reported in December 2019 at Wuhan, China. This virus critically affects several countries such as the USA, Brazil, India and Italy. Numerous research units are working at their higher level of effort to develop novel methods to prevent and control this pandemic scenario. The main objective of this paper is to propose a medical decision support system using the implementation of a convolutional neural network (CNN). This CNN has been developed using EfficientNet architecture. To the best of the authors' knowledge, there is no similar study that proposes an automated method for COVID-19 diagnosis using EfficientNet. Therefore, the main contribution is to present the results of a CNN developed using EfficientNet and 10-fold stratified cross-validation. This paper presents two main experiments. First, the binary classification results using images from COVID-19 patients and normal patients are shown. Second, the multi-class results using images from COVID-19, pneumonia and normal patients are discussed. The results show average accuracy values for binary and multi-class of 99.62% and 96.70%, respectively. On the one hand, the proposed CNN model using EfficientNet presents an average recall value of 99.63% and 96.69% concerning binary and multi-class, respectively. On the other hand, 99.64% is the average precision value reported by binary classification, and 97.54% is presented in multi-class. Finally, the average F1-score for multi-class is 97.11%, and 99.62% is presented for binary classification. In conclusion, the proposed architecture can provide an automated medical diagnostics system to support healthcare specialists for enhanced decision making during this pandemic scenario.