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Due to society aging, age-related issues such as mild cognitive impairments (MCI) and dementia are attracting the attention of health professionals, scientists and governments. Seniors suffering from such impairments notice a slight deterioration in their cognitive abilities, which may lead to memory loss and behavioural disorders. In consequence, such seniors refrain from doing their everyday outdoor activities. Technology, e.g. smartphones, wearables and artificial intelligence, can provide seniors and their relatives with a variety of monitoring tools. In a nutshell, locations are analysed and, under specific situations, alarms are raised so that caregivers urgently informed. In this context, the discovery and prediction of trajectories and behaviours play a key role in deploying effective monitoring solutions. In this paper, we present a real-time smartphone-based monitoring system, called SafeMove, to discover and predict elderly people behaviours by analyzing outdoor trajectories. This is achieved by firstly analysing the elder’s mobility data previously collected using the proposed model called SpaceTime-Convolutional Neural Network (ST-CNN) in order to predict the most popular locations he/she might visit in the next time. Based on the predicted locations, the elder can be monitored in bounded region. Time and space-related variables, such as the distance traversed, the direction of the movements and the time spent, are analyzed in our abnormal behaviour detection (ABD) model that takes advantage of recurrent neural networks (RNNs). The effectiveness and the efficiency of our system for predicting the next location and detection the abnormal behaviors are evaluated using different datasets comprising real-world GPS trajectories.

Intelligent transportation systems will play a key role in the smart cities of the future. In particular, railway transportation is gaining attention as a promising solution to cope with the mobility challenges in large urban areas. Thanks to the miniaturisation of sensors and the deployment of fast data networks, the railway industry is being augmented with contextual, real-time information that opens the door to novel and personalised services. Despite the benefits of this digitalisation, the high complexity of railway transportation entails a number of challenges, particularly from security and privacy perspectives. Since railway assets are attractive targets for terrorism, coping with strong security and privacy requirements such as cryptography and privacy-preserving methods is of utmost importance. This article provides a thorough systematic literature review on information security and privacy within railway transportation systems, following the well-known methodology proposed by vom Brocke et al. We sketch out the most relevant studies and outline the main focuses, challenges and solutions described in the literature, considering technical, societal, regulatory and ethical approaches. Additionally, we discuss the remaining open issues and suggest several research lines that will gain relevance in the years to come.

To analyze the temporal trends of atrial fibrillation (AF)-related ischemic stroke (IS) and their relationship with the prescription patterns of antithrombotic treatment from 2013 to 2019 in the Health Assistance Area of a regional hospital.First, a retrospective ecological study of aggregate data to analyze the annual incidence of IS between 2013 and 2019 was performed. Second, we selected those patients diagnosed with AF between 2013 and 2019 and performed a retrospective longitudinal study to assess the role of antithrombotic therapy in the development of AF-related IS.During this period, whereas the annual incidence of IS remained stable (from 1.3 in 2013 to 1.2 cases per 1000 inhabitants in 2019; adjustedThe prescription of oral anticoagulants, particularly direct oral anticoagulants, has increased from 2013 to 2019 in our Health Assistance Area. This increase might partially explain the reduction in AF-related IS.

Introduction: In patients with heart failure, global longitudinal strain (GLS) early detects decreased ventricular contractility, with prognostic value, but there is no evidence that GLS properly differentiates etiologies in patients with left ventricular ejection fraction

The evaluation and accreditation of competencies is one of the challenges that higher education must address in the 21st century. GRAF is a system of evaluation and accreditation of curricular competencies based on the integration of pedagogical engineering methodologies with data architecture design and technological applications, especially tailored to online higher education. From the teaching point of view, it provides a model, a methodology, and a tool for designing competency-based programmes and assessing the evidence of competency learning generated by the students in the teaching and learning process throughout the semester. From the student’s perspective, it enhances conventional evaluation by accompanying marks with educational feedback on the level of competency achieved, and provides a graphic representation of progress. Specific tools and applications have been developed for each phase of work: for the design stage, the Competency Design Tool; for the evaluation and monitoring stage, the Competency Assessment Tool; and for the graphic representation stage, the Competency Graph Report. The Universitat Oberta de Catalunya (UOC) has spent two years developing GRAF. It has been applied to five programmes in different areas: an MBA, two master’s degrees, a postgraduate course, and a university extension course. A total of 56 compulsory subjects and 293 students have been involved in these programmes. Due to the success of the initiative, the UOC has decided on a widespread application of GRAF to other programmes. We are interested in sharing the experience and lessons learned in order to improve GRAF.

Critically ill patients that stay in Intensive Care Units (ICU) for long periods suffer from Post-Intensive Care Syndrome or ICU Acquired Weakness, whose effects can decrease patients’ quality of life for years. To prevent such issues and aiming at shortening intensive care treatments, Early Mobilisation (EM) has been proposed as an encouraging technique: the literature includes numerous examples of the benefits of EM on the prevention of post-operative complications and adverse events. However, the appropriate application of EM programmes entails the use of scarce resources, both human and technical. Information and Communication Technologies can play a key role in reducing cost and improving the practice of EM. Although there is rich literature on EM practice and its potential benefits, there are some barriers that must be overcome, and technology, i.e., the use of sensors, robotics or information systems, can contribute to that end. This article reviews the literature and analyses on the use of technology in the area of EM, and moreover, it proposes a smart health-enhanced scenario.

The understanding of human ageing contributes to the overall improvement of healthcare and opens the door to the increase of life expectancy and quality of life. Studies on human ageing, which are evidence-based, require data. Although some efforts have been put to concentrate datasets in single reference points, many datasets remain hidden in articles, studies and research projects websites. In this paper, we aim at identifying those datasets that have been used for the study of human ageing and make them easy to find to researchers in the field. To do so, we have analysed wellknown literature databases, previous reviews, and specialised sources. We have reviewed the available data and, as a result, we have identified and discussed 28 datasets. Hence, this article provides an organised reference point to datasets for researchers interested in the study of human ageing and contributes to their visibility.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a very contagious virus, has led to the coronavirus disease 2019 (COVID-19) pandemic. The clinical manifestations of this virus in humans vary widely, from asymptomatic to severe, with diverse symptomatology and even death. The substantial transmission from asymptomatic people has facilitated the widespread transmission of SARS-CoV-2, hampering public health initiatives to identify and isolate infected people during the pre-symptomatic contagious period. COVID-19 is associated with cardiac complications that can progress from mild to life-threatening. The aim of this article is to analyse the present knowledge of COVID-19 and cardiac involvement, the development of arrhythmia risk and its treatment.

The human normal heart rhythm occurs when a person has a regular heart beat originated by depolarization of the sinus node, which is normally transmitted through the atria to the AV node, and thereafter normally using the His-Purkinje system to the myocytes of the ventricles. This normal heart beat occurs in a rate which is appropriated to the clinical circumstances of the individual (e.g. faster during exercise than at rest) and is called sinus rhythm. The term arrhythmia comprises a group of heterogeneous conditions with abnormal electrical activity of the heart in origin, rate or both (absence of sinus rhythm). Arrhythmias may be clinically silent (at least during a period) or cause symptoms and signs (palpitations, syncope or even sudden cardiac death). The terms bradycardia and tachycardia refer to a deficit and to an excess of heart rate (compared to normal), respectively.

Traditionally, antiarrhythmic strategies have focused almost exclusively on atrial electrophysiological alterations. Unfortunately, these interventions yield very limited success over the long-term and have a high side-effect profile. A number of ‘non-antiarrhythmic’ drugs (i.e., whose main effect is not exerted at ion channel level) have gained increasing attention due to their potential to prevent, delay, or even reverse atrial fibrillation (AF)-related atrial structural remodeling, while lacking the undesirable effects of ion channel blockers. By modifying the substrate upstream of AF, these new strategies are expected to prevent new-onset AF, to delay AF transition to more persistent forms, and/or to prevent recurrent AF. However, to date, with very few the exceptions, there is insufficient evidence to support the wide use of non-conventional antiarrhythmic drugs for AF prophylaxis in clinical practice. It still remains to be established whether this approach is truly effective, by itself or at least in addition to conventional rhythm control strategies. Clarification of the most adequate target population, of the most suitable drug, dose, and timing to intervene is also required.

Predicting people’s next location has attracted the attention of both scientists and large Internet companies, for a variety of reasons. The analysis of location data collected from smart mobile devices paves the way for the improvement of current location based services and the rise of new business models, based on rich notifications related to the right prediction of users’ next location. Moreover, the so-called attention technique has been adopted in neural networks learning, aiming at considering alignments between different parts of the source training data. This article proposes the model Move, Attend and Predict (MAP) to predict a person’s future location based on his/her mobility pattern collected by a mobile device. This is achieved by means of a computationally efficient trainable deep neural network model. Our model essentially learns which time interval in the trajectory sequences are relevant regarding a specific location. In order to extract the meaningful representation from trajectories and time sequences, the embedding representation learning technique is used. Experimental results, obtained from tests conducted on two large real-life datasets, demonstrate that our model outperforms state-of-the-art models in terms of precision, recall and F1-score performance metrics.

Location-Based Services (LBSs) provide users especially in smartphones with information and services based on their location and interests. Predicting people’s next location could help in improving the quality of such services and, in turn, boosting people’s confidence on those services. Any successful LBS algorithm or model should target three major goals: Location prediction accuracy, high throughput or fast response and efficiency in terms of utilizing smartphone resources. This paper proposes a new approach to discover and predict people’s next location based on their mobility patterns, while being computationally efficient. The approach starts by discovering Regions-of-Interest (RoIs) in people’s historical trajectories (which denote the locations where users have been previously, frequently). A new model based on Markov Chain (MC) is proposed to overcome the drawback of classical MC. Our proposed model considers both space and time contexts where the space represents a specific location that has been visited by a user while the time represents the location visiting time. We show how classical MC model can be extended to include movement times and how time will improve prediction accuracy. One unique finding in our research is related to the value of integrating users’ mobility location/space with time context. In particular, time context is formulated in a way to add extra information to the space context. For better abstraction during building the model, a general transformation function is used to transform the n -order MC into first order. Results show that the proposed approach provides significant improvements in predicting people’s next location compared with the state-of-the-art models when applied on two large real-life datasets, GeoLife and Gowalla.

The advances in the miniaturisation of electronic devices and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments have opened the door to numerous opportunities for providing added-value, accurate and personalised services to citizens. In particular, smart healthcare, regarded as the natural evolution of electronic health and mobile health, contributes to enhance medical services and people’s welfare, while shortening waiting times and decreasing healthcare expenditure. However, the large number, variety and complexity of devices and systems involved in smart health systems involve a number of challenging considerations to be considered, particularly from security and privacy perspectives. To this aim, this article provides a thorough technical review on the deployment of secure smart health services, ranging from the very collection of sensors data (either related to the medical conditions of individuals or to their immediate context), the transmission of these data through wireless communication networks, to the final storage and analysis of such information in the appropriate health information systems. As a result, we provide practitioners with a comprehensive overview of the existing vulnerabilities and solutions in the technical side of smart healthcare.

Smart cities have sensors and communication infrastructures that provide humongous amounts of data. People can extend this infrastructure, acting as independent, mobile sensors through their smartphones, enabling opportunistic sensing. This information provides locality and timeliness of measurements that would otherwise be unavailable. Researchers have developed a context-aware healthy-route recommender system that offers personalized recommendations to people according to their medical condition and real-time information from the smart city. Experiments with a simulated dataset and real data verified the system’s usefulness.

Isobaric vapor–liquid equilibria for the 1-propanol + 1-ethyl-3-methylimidazolium dicyanamide ([emim [DCA] binary system and for the 1-propanol + water + [emim] [DCA] ternary system have been obtained at 100 kPa using a recirculating still. The electrolyte nonrandom two-liquid (e-NRTL) model was used for fitting successfully the experimental data. The effect of [emim][DCA] on the 1-propanol + water system has been compared with that produced by other ionic liquids reported in the literature. From the results, [emim][DCA] appears as a potential entrainer for the extractive distillation of this solvent mixture, causing the azeotrope to disappear at 100 kPa when the ionic liquid mole fraction is greater than 0.33.

Isobaric vapor–liquid equilibria for the binary systems 2-propanol + water, 2-propanol + 1-ethyl-3-methylimidazolium dicyanamide ([emim][DCA]), and water + [emim][DCA] as well as the vapor–liquid equilibria for the 2-propanol + water + [emim][DCA] ternary system have been obtained at 100 kPa using a recirculating still. The electrolyte nonrandom two-liquid (e-NRTL) model was used for fitting successfully the experimental data. The effect of [emim][DCA] on the 2-propanol + water system has been compared with that produced by other ionic liquids reported in the literature. From the results, [emim][DCA] appears as a good entrainer for the extractive distillation of this solvent mixture, causing the azeotrope to disappear at 100 kPa when the ionic liquid mole fraction is greater than 0.085.

Worldwide, thousands of video surveillance cameras record our daily activities. People are aware that video surveillance is deployed for the sake of security. However, the privacy of individuals would be endangered if the proper measures were not considered. Privacy-aware video surveillance has historically been addressed by proposals based on detecting individuals and other sensitive parts of the video and hiding them using a variety of techniques. In this paper, we present a comprehensive solution tackling video processing, video protection and management of the Information System. We claim that a video surveillance system can protect our safety and, at the same time, guarantee our privacy. We describe the design and implementation of a privacy-aware video surveillance platform that, in order to be trustworthy, accomplishes with the properties of high detection accuracy, real-time performance and protected video utility. We have tested the proposed platform, and we demonstrate the feasibility of our approach for privacy protection.

Process Mining comprises a consolidated set of techniques that aim at discovering, checking and improving processes. Originally designed to cope with business processes, process mining techniques fed on log files automatically generated by enterprise resource planning (ERP) systems and software alike. However, nowadays, with the unstoppable deployment of the Internet of Things, multiple events are continuously generated and stored by distributed, highly uncoupled and heterogeneous devices able to sense their surroundings and report precise and timely updates on the environment. By embracing the Internet of Events, recently available contextual information opens the door to the application of process mining techniques in new domains, hence, generating opportunities for the analysis of contextual data with multiple implications in hot research areas such as, smart environments (e.g., smart cities, smart buildings, smart homes), cognitive systems, and industry 4.0. In this article, we discuss the opportunities and challenges of process mining in these new, events-rich, context-aware environments.

The ideal pathway of care for heart failure patients is a key feature of this consensus document. It offers a pathway of care that is the foundation from which to assess barriers that are context-specific.The roadblocks and potential solutions to patient care are offered based on key stages of the patient care pathway. They have been accumulated from previous WHF Roadmaps, built on the experience of the core writing group, and based on consensus and feedback from a wider survey process.Evidence-based solutions to specific roadblocks are offered in tables within the document. The barriers to care are offered from the highest rated barriers from the WHF Survey on the Heart Failure Roadmap and from 146 experts.

Early Mobilisation routines aim at improving the recovery process of critically ill patients that are hospitalised in intensive care units. Such routines consist of passive and active progressive mobilisations aiming at reducing the side-effects derived from ICU stays. In this paper, we present e-PEMICU, an e-health platform to support early mobilisation programmes in intensive care units. Our solution is founded on motion sensors and smartphones, hence, it is affordable and easy to deploy in real scenarios. We present the design and implementation of a prototype that has been created to address real users needs. Our platform paves the way for a better and sustainable application of current early mobilisation routines.

The healthcare sector is rapidly changing to incorporate new technologies Antoni that enable the collection of unprecedented amounts of data. New paradigms such as mobile and smart healthcare are founded on these new technologies, such as the IoT, and the data they collect are no longer only medical but, instead, they refer to contextual information with spatio-temporal features. Also, new data processing techniques, such as process mining, are gaining attention and are reshaping the landscape of healthcare information systems. In this article, we concentrate on Geographic Information Systems and propose HGIS: a comprehensive, healthcare-oriented, GIS architecture to support the integration of heterogeneous data with spatio-temporal features. Moreover, we comment on initial results obtained from a proof-of-concept implementation developed by Xarxa Sanitaria i Social de Santa Tecla, a large healthcare provider in the south of Catalonia.

This book of proceedings gathers the contributions presented at the 5th URV Doctoral Workshop in Computer Science and Mathematics. The main aim of this workshop is to promote the dissemination of the ideas, methods and results that are developed in the Doctoral Thesis of the students of this doctorate program, and to promote the knowledge sharing, collaboration and discussion between their respective research groups.

Aim: To analyze the impact of implementing a program integrating cardiology and primary care in clinical practice. Methods: In the integrated care model, every cardiologist was assigned to each primary care center. Results & conclusion: The implementation of the new care model was associated with a significant reduction of 31.2% in requests of first visits. In addition, the delay to the cardiologist consultation significantly decreased by 54.5% for the first visits, and by 57.1% for the follow-up visits. The proportion of patients that achieved recommended low density lipoprotein-cholesterol goals significantly increased from 20.8 to 29.6%. The proportion of patients submitted to anticoagulant therapy significantly increased from 69.3 to 74.2%, as well as the proportion of patients taking direct oral anticoagulants (from 7.9 to 28.4%).

Face de-identification aims at preserving the privacy of people by concealing faces in images and videos. In this paper, we propose a defeating algorithm for face de-identification methods that are based on DCT-block scrambling. These methods protect faces by scrambling the AC and DC coefficients of the DCT blocks corresponding to a face region in the compressed domain. The proposed approach does not make use of the protection key utilized in the de-identification process. It consists of the following stages. First, random unprotected faces are generated based on a random alteration of the sign of AC coefficients with a fixed value of DC coefficients. Then, the best unprotected faces are selected by an eigenfaces model trained with facial images from a repository of potentially protected people. A single facial image is then generated by merging the selected images through median stacking. Finally, the eigenfaces model is utilized again to choose the face from the repository that is closest to the resulting image in order to improve the aspect of the unprotected face. Experimental results using a proprietary database and the public CALTECH, Utrecht and LFW face databases show the effectiveness of the proposed technique.

It is essential to prevent thromboembolic events in atrial fibrillation. The risks of thromboembolic and haemorrhagic events must be carefully assessed and weighed against one another, both in routine situations and in relation to invasive procedures. Vitamin K antagonists, until recently the first-line treatment for prophylaxis against thromboembolic events in patients with atrial fibrillation, have various drawbacks. Direct-acting oral anticoagulants overcome these limitations and are efficacious and safe. The recent developments of tests to monitor anticoagulant levels, and of target-specific reversal agents for these newer drugs, has facilitated their use in several situations, including emergencies. For these reasons, the European Society of Cardiology and other scientific societies now recommend direct-acting oral anticoagulants as first-line treatment for preventing thromboembolic events in atrial fibrillation.

Anemia and iron deficiency are often associated with heart failure, influencing the symptoms and prognosis. Correction of anemia and iron deficiency improves functional capacity and decreases hospitalizations. Many studies have analyzed echocardiographic parameters in iron deficiency and anemia and their evolution after iron treatment; however, the heterogeneity of the results makes it difficult to draw conclusions. The aim of this paper is to review the echocardiographic parameters during anemia and iron deficiency, and their evolution after treatment. Available data suggest that they lead to ventricular and atrial remodeling, a decrease in ventricular contractility, and an alteration of ventricular relaxation, although in heart failure with preserved ejection fraction these changes are not significant. Anemia and iron deficiency also increase systolic pulmonary artery pressure. There is consistent evidence that correction of these comorbidities leads to a reduction in preload and left ventricular cavity dimensions, an improvement in diastolic and load-independent ventricular systolic function parameters, and a decrease in systolic pulmonary artery pressure. However, the evidence is less consistent about the changes produced in ventricular hypertrophy, load-dependent systolic function parameters, and E-wave. Generally, anemia and iron deficiency affect the echocardiographic findings, and correcting these conditions often results in improvement in the affected echocardiographic parameters.

Smart cities are progressively gaining importance due to the worldwide process of urbanisation that is taking place and the need for more sustainable and liveable cities. In the context of a smart city, networks of interconnected heterogeneous devices provide lots of data. In addition, citizens have the possibility of using their smartphones to share information with the city and with other users, thus contributing to the overall infrastructure. With the aim to use the infrastructure of modern smart cities to improve the quality of life of citizens and their healthcare system, the concept of smart health (s-health) was born. In this article, we show how recommendation systems could be used to provide healthcare services within the context of a smart city in which citizens collaborate with the city to improve their quality of life. We describe the architecture and implementation details of a smart health application and set the foundations for further research lines that are briefly pointed out.

The existence of certain comorbidities, the co-occurrence of different diseases in the same individual, is well-known in the medical community. However, finding temporal cause-effect relations between those diseases constitutes a big challenge. Clinical records can be used to extract the required information, but their analysis is elusive due to the vast and heterogeneous amount of data. We propose a new methodology for time-preserving tensor networks decomposition to be applied in the analysis of big data problems where the temporal dimension of the key factual fields must not be modified. This methodology will also allow the creation of a new process mining modeling which can capture the cause-effect relations as low-order tensors associated to the transitions of the mined processes, and whose structure takes the form of a multilayer complex network. All these theoretical and methodological advances will allow their application to real biomedical data to analyze comorbidities.

Smartphones are used worldwide for a variety of purposes: constant communication, immediate access to a humongous amount of knowledge, effortless ways of posting pictures, feelings and thoughts to name a few. In consequence, smartphones have become our natural life companions and, thanks to the rise of concepts such as mobile health and smart health, will be a linchpin regarding our care. Detecting the appearance of wandering behaviours in the elderly population is crucial in order to tackle the early treatment of mild cognitive impairments. To that end, a number of proposals have appeared, either theoretical methods or practical applications. This paper addresses the assessment of wandering detection methods from different perspectives. First, we review the available datasets in the literature that can be used for benchmarking wandering detection methods. Moreover, we analyse the available datasets using a well-known wandering detection technique, so as to obtain the number of abnormal trajectories with respect to the whole dataset and each individual. Finally, we discuss the properties that a dataset for benchmarking should fulfil.

The detection of cry is crucial in intelligent computerized systems that aim at assessing the well-being of neonates during their hospitalization periods. Moreover, a precise characterization of cry allows its classification (e.g., hunger, pain, tiredness…). Although several cry detection and characterization techniques can be found in the literature, there is no testing of such techniques in real-life environments such as hospital intensive care units. In this article, we first summarize the problem of background noise in intensive care units that may prevent the operation of cry detection algorithms from succeeding. Second, we implement a specific cry detection technique that is based on some of the relevant cry detection proposals that have been found in the literature. Finally, we test this method using audio samples recorded in a real neonatal intensive care unit.

Densities, speeds of sound, viscosities and refractive indices of two binary systems 1-butyl-3-methylimidazolium tetrafluoroborate [bmim][BF4] + methanol and 1-ethyl-3-methylimidazolium tetrafluoroborate [emim][BF4] + methanol, as well as of all pure components, have been measured covering the whole range of compositions at T = (278.15 to 318.15) K and p = 101 kPa. From this data, excess molar volumes, excess isentropic compressibilities, viscosity deviations and refractive index deviations were calculated and fitted to extended versions of the Redlich–Kister equation. Estimated coefficients of these equations taking into account the dependence on composition and temperature simultaneously were also presented.

Recent advances in pervasive video surveillance systems pave the way for a comprehensive surveillance of every aspect of our lives, hence, leading us to a state of dataveillance. Computerized and interconnected systems of cameras could be used to profile, track and monitor individuals for the sake of security. Notwithstanding, these systems clearly interfere with the fundamental right of the individuals to privacy. Most literature on privacy in video surveillance systems concentrates on the goal of detecting faces and other regions of interest and in proposing different methods to protect them. However, the trustworthiness of those systems and, by extension, of the privacy they provide are mostly neglected. In this article, we define the concept of trustworthy privacy-aware video surveillance system. Moreover, we assess the techniques proposed in the literature according to their suitability for such a video surveillance system. Finally, we describe the properties that a deployment of a trustworthy video surveillance system must fulfill.

Mas alla de los resultados de los ensayos clinicos (RE-LY, ROCKET-AF, ARISTOTLE y ENGAGE AF-TIMI 48), que se comentan mas adelante, la aparicion de los nuevos anticoagulantes orales (NACO) ha supuesto un gran avance en la prevencion de las complicaciones tromboembolicas en el paciente con fibrilacion auricular (FA) no valvular con respecto al tratamiento estandar, los antagonistas de la vitamina K (warfarina y acenocumarol). Los NACO (dabigatran, rivaroxaban y apixaban estan actualmente disponibles en la practica clinica y edoxaban probablemente lo estara) superan en gran medida las desventajas que clasicamente se habian asociado a los antagonistas de la vitamina K (estrecha ventana terapeutica, respuesta variable, multiples interacciones con los alimentos y otros farmacos, y comienzo y final de accion lentos). Las consecuencias mas inmediatas y evidentes de estas limitaciones son la necesidad de realizar controles periodicos y continuos ajustes de dosis, ademas de restricciones en la alimentacion y tener un cuidado exquisito con la medicacion concomitante que se prescribe. Como consecuencia de todo ello, la calidad de vida de los pacientes se ve limitada en gran medida. Pero lo mas importante es que, ademas, hace que muchos pacientes con FA que tienen indicacion clara de anticoagulacion no la esten recibiendo. Es mas, incluso entre los pacientes anticoagulados con antagonistas de la vitamina K, aproximadamente un 35–40% tiene mal control habitual de la razon normalizada internacional (INR) en Espana, con las consecuencias nefastas que esto puede tener en cuanto al riesgo tanto de ictus como de sangrados. Los NACO, al tener una amplia ventana terapeutica, una respuesta anticoagulante predecible, sin interferencias con los alimentos, y muy escasas interacciones con otros farmacos, permiten una anticoagulacion constante y previsible, sin necesidad de monitorizacion periodica de la respuesta anticoagulante ni de ajustes de dosis repetitivos.

10.1109/MCOM.2014.6871673 The new era of mobile health ushered in by the wide adoption of ubiquitous computing and mobile communications has brought opportunities for governments and companies to rethink their concept of healthcare. Simultaneously, the worldwide urbanization process represents a formidable challenge and attracts attention toward cities that are expected to gather higher populations and provide citizens with services in an efficient and human manner. These two trends have led to the appearance of mobile health and smart cities. In this article we introduce the new concept of smart health, which is the context-aware complement of mobile health within smart cities. We provide an overview of the main fields of knowledge that are involved in the process of building this new concept. Additionally, we discuss the main challenges and opportunities that s-Health would imply and provide a common ground for further research.

New oral anticoagulants (NOAC) have demonstrated their efficacy as an alternative to vitamin K antagonists (VKA) in the prophylaxis of cardioembolic events in patients with atrial fibrillation (AF). However, evidence on the benefits of NOAC in health-related quality of life (HRQoL) is lacking. We evaluated changes in HRQoL related to oral anticoagulation therapy employing a specific questionnaire in a cohort of 416 patients with AF undergoing electrical cardioversion. In terms of HRQoL, we observed a progressive adaptation to treatment with VKA; satisfaction with NOAC remained constant. Older age, higher left ventricular ejection fraction and NOAC were associated with better HRQoL.

In this paper, we present the concept of double-phase microaggregation as an improvement of classical microaggregation for the protection of privacy in distributed scenarios without fully trusted parties. We apply this new concept in the context of mobile health and we show that a distributed architecture consisting of patients and several intermediate entities can apply it to protect the privacy of patients, whose data are released to third parties for secondary use. After recalling some fundamental concepts of statistical disclosure control and microaggregation, we detail the distributed architecture that allows the private gathering, storage, and sharing of biomedical data. We show that double-phase multivariate microaggregation properly fits the needs for privacy preservation of biomedical data in the distributed context of mobile health. Moreover, we show that double-phase microaggregation performs similarly to classical microaggregation in terms of information loss, disclosure risk, and correlation preservation, while avoiding the limitations of a centralized approach.

Resumen La fibrilacion auricular es la arritmia cardiaca mas frecuente en la practica clinica diaria. Ademas de las alteraciones hemodinamicas que ocasiona —consecuencia de la perdida de la contraccion auricular y la frecuencia cardiaca habitualmente elevada, que pueden causar la aparicion de insuficiencia cardiaca—, el principal riesgo de la fibrilacion auricular es que la estasis circulatoria en la auricula cause una embolia arterial. Se sabe que la fibrilacion auricular se asocia a un marcado aumento del riesgo de accidentes tromboembolicos arteriales, asociados a elevadas mortalidad y morbilidad y alto riesgo de recurrencia. Esta demostrado que el tratamiento antitrombotico con anticoagulantes orales se asocia a un drastico descenso del riesgo de accidentes tromboembolicos. Sin embargo, los anticoagulantes orales incrementan significativamente el riesgo de hemorragias mayores, de las que es especialmente devastadora la hemorragia intracraneal. Por lo tanto, al iniciar el tratamiento anticoagulante de un paciente con fibrilacion auricular, resulta imprescindible valorar adecuadamente el beneficio/riesgo del tratamiento segun sus caracteristicas clinicas. Multiples estudios han mostrado una serie de variables que determinan los riesgos emboligeno y de sangrado asociados al tratamiento anticoagulante, y con base en ellas se han desarrollado diversos sistemas de estratificacion para calcular el riesgo de embolia secundaria a la fibrilacion auricular y el riesgo de hemorragia relacionado con el tratamiento antitrombotico. En la practica clinica diaria, la aplicacion de estas escalas de riesgo es de gran ayuda para elegir la mejor alternativa terapeutica para un paciente concreto.

Resumen La fibrilacion auricular es una arritmia que se caracteriza por la contraccion de las fibras auriculares de modo caotico e impredecible. Esta disincronia origina la perdida funcional de contractilidad. Por ello, se favorece la coagulacion intraauricular y, consecuentemente, se aumenta la probabilidad de sufrir un ictus por tromboembolia. Es bien conocido que esta arritmia aumenta la mortalidad y la morbilidad. Por lo tanto, su prevencion y manejo son de gran importancia tanto para los pacientes como para el sistema sanitario. Este articulo analiza los datos epidemiologicos de la fibrilacion auricular en Espana.

In this work, densities and sound velocities of three ionic liquids, 3-butyl-1-ethylimidazolium trifluoromethanesulfonate, 3-butyl-1-methylimidazolium trifluoromethanesulfonate, and 1-butyl-1-methylpyrrolidinium trifluoromethanesulfonate, and their binary mixtures with water have been obtained at p = (101 ± 2) kPa and T = (278.15 to 338.15) K, covering the whole range of concentration. The isentropic compressibility and molar isentropic compression of pure components and mixtures have been calculated using the Newton–Laplace equation. Moreover, the excess molar volume and excess molar isentropic compression of mixtures have been then determined, and they have been fitted to temperature and composition by an extended Redlich–Kister equation. Optimal fitting parameters have been reported.

Isobaric vapor–liquid equilibria (VLE) for the ternary system acetone + methanol +1-ethyl-3-methylimidazolium dicyanamide ([emim][DCA]) as well as the two solvent + IL binary systems have been obtained at 100 kPa using a recirculating still. The addition of [emim][DCA] to the solvent mixture produced a salting-out effect greater than that produced by other ionic liquids, showing that this ionic liquid is, until now, the best IL tested as an entrainer for the extractive distillation of the acetone + methanol mixtures, causing the azeotrope to disappear for an ionic liquid mole fraction as low as 0.031, at 100 kPa. This behavior can be explained on the basis of the influence of the IL concentration on the activity coefficient of each solvent in the binary mixtures. The electrolyte nonrandom two-liquid (NRTL) model was used for fitting successfully the experimental data.