ObjectiveTo assess the prevalence of burn-out syndrome in healthcare workers working on the front line (FL) in Spain during COVID-19.DesignCross-sectional, online survey-based study.SettingsSampling was performed between 21st April and 3rd May 2020. The survey collected demographic data and questions regarding participants’ working position since pandemic outbreak.ParticipantsSpanish healthcare workers working on the FL or usual ward were eligible. A total of 674 healthcare professionals answered the survey.Main outcomes and measuresBurn-out syndrome was assessed by the Maslach Burnout Inventory-Medical Personnel.ResultsOf the 643 eligible responding participants, 408 (63.5%) were physicians, 172 (26.8%) were nurses and 63 (9.8%) other technical occupations. 377 (58.6%) worked on the FL. Most participants were women (472 (73.4%)), aged 31–40 years (163 (25.3%)) and worked in tertiary hospitals (>600 beds) (260 (40.4%)). Prevalence of burn-out syndrome was 43.4% (95% CI 39.5% to 47.2%), higher in COVID-19 FL workers (49.6%, p<0.001) than in non- COVID-19 FL workers (34.6%, p<0.001). Women felt more burn-out (60.8%, p=0.016), were more afraid of self-infection (61.9%, p=0.021) and of their performance and quality of care provided to the patients (75.8%, p=0.015) than men. More burn-out were those between 20 and 30 years old (65.2%, p=0.026) and those with more than 15 years of experience (53.7%, p=0.035).Multivariable logistic regression analysis revealed that, working on COVID-19 FL (OR 1.93; 95% CI 1.37 to 2.71, p<0.001), being a woman (OR 1.56; 95% CI 1.06 to 2.29, p=0.022), being under 30 years old (OR 1.75; 95% CI 1.06 to 2.89, p=0.028) and being a physician (OR 1.64; 95% CI 1.11 to 2.41, p=0.011) were associated with high risk of burn-out syndrome.ConclusionsThis survey study of healthcare professionals reported high rates of burn-out syndrome. Interventions to promote mental well-being in healthcare workers exposed to COVID-19 need to be immediately implemented.
By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks.
Abstract. The problem of recurring concepts in data stream classification is a special case of concept drift where concepts may reappear. Although several existing methods are able to learn in the presence of concept drift, few consider contextual information when tracking recurring concepts. Nevertheless, in many real-world scenarios context information is available and can be exploited to improve existing approaches in the detection or even anticipation of recurring concepts.In this work, we propose the extension of existing approaches to deal with the problem of recurring concepts by reusing previously learned decision models in situations where concepts reappear. The different underlying concepts are identified using an existing drift detection method, based on the error-rate of the learning process. A method to associate context information and learned decision models is proposed to improve the adaptation to recurring concepts. The method also addresses the challenge of retrieving the most appropriate concept for a particular context. Finally, to deal with situations of memory scarcity, an intelligent strategy to discard models is proposed. The experiments conducted so far, using synthetic and real datasets, show promising results and make it possible to analyze the trade-off between the accuracy gains and the learned models storage cost.
The increasing power of computer technology does not dispense with the need to extract meaningful information out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now commonly used in the scientific community: data mining and complex network theory. Not only do complex network analysis and data mining share the same general goal, that of extracting information from complex systems to ultimately create a new compact quantifiable representation, but they also often address similar problems too. In the face of that, a surprisingly low number of researchers turn out to resort to both methodologies. One may then be tempted to conclude that these two fields are either largely redundant or totally antithetic. The starting point of this review is that this state of affairs should be put down to contingent rather than conceptual differences, and that these two fields can in fact advantageously be used in a synergistic manner. An overview of both fields is first provided, some fundamental concepts of which are illustrated. A variety of contexts in which complex network theory and data mining have been used in a synergistic manner are then presented. Contexts in which the appropriate integration of complex network metrics can lead to improved classification rates with respect to classical data mining algorithms and, conversely, contexts in which data mining can be used to tackle important issues in complex network theory applications are illustrated. Finally, ways to achieve a tighter integration between complex networks and data mining, and open lines of research are discussed.
We introduce a novel method to represent time independent, scalar data sets as complex networks. We apply our method to investigate gene expression in the response to osmotic stress of Arabidopsis thaliana. In the proposed network representation, the most important genes for the plant response turn out to be the nodes with highest centrality in appropriately reconstructed networks. We also performed a target experiment, in which the predicted genes were artificially induced one by one, and the growth of the corresponding phenotypes compared to that of the wild-type. The joint application of the network reconstruction method and of the in vivo experiments allowed identifying 15 previously unknown key genes, and provided models of their mutual relationships. This novel representation extends the use of graph theory to data sets hitherto considered outside of the realm of its application, vastly simplifying the characterization of their underlying structure.
Abstract-Mobile activity recognition focuses on inferring the current activities of a mobile user by leveraging the sensory data that is available on today's smart phones. The state of the art in mobile activity recognition uses traditional classification learning techniques. Thus, the learning process typically involves: i) collection of labelled sensory data that is transferred and collated in a centralised repository; ii) model building where the classification model is trained and tested using the collected data; iii) a model deployment stage where the learnt model is deployed on-board a mobile device for identifying activities based on new sensory data. In this paper, we demonstrate the Mobile Activity Recognition System (MARS) where for the first time the model is built and continuously updated on-board the mobile device itself using data stream mining. The advantages of the on-board approach are that it allows model personalisation and increased privacy as the data is not sent to any external site. Furthermore, when the user or its activity profile changes MARS enables promptly adaptation. MARS has been implemented on the Android platform to demonstrate that it can achieve accurate mobile activity recognition. Moreover, we can show in practise that MARS quickly adapts to user profile changes while at the same time being scalable and efficient in terms of consumption of the device resources.
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