Federated Robust Embedded Systems: Concepts and Challenges

The development within the area of embedded systems (ESs) is moving rapidly, not least due to falling costs of computation and communication equipment. It is believed that increased communication opportunities will lead to the future ESs no longer being parts of isolated products, but rather parts of larger communities or federations of ESs, within which information is exchanged for the benefit of all participants. This vision is asserted by a number of interrelated research topics, such as the internet of things, cyber-physical systems, systems of systems, and multi-agent systems. In this work, the focus is primarily on ESs, with their specific real-time and safety requirements. While the vision of interconnected ESs is quite promising, it also brings great challenges to the development of future systems in an efficient, safe, and reliable way. In this work, a pre-study has been carried out in order to gain a better understanding about common concepts and challenges that naturally arise in federations of ESs. The work was organized around a series of workshops, with contributions from both academic participants and industrial partners with a strong experience in ES development. During the workshops, a portfolio of possible ES federation scenarios was collected, and a number of application examples were discussed more thoroughly on different abstraction levels, starting from screening the nature of interactions on the federation level and proceeding down to the implementation details within each ES. These discussions led to a better understanding of what can be expected in the future federated ESs. In this report, the discussed applications are summarized, together with their characteristics, challenges, and necessary solution elements, providing a ground for the future research within the area of communicating ESs

Improving high availability and reliability of health interoperability systems

The accessibility and availability of patient clinical information are a constant need. The Agency for Interoperation, Diffusion and Archive of Medical Information (AIDA) was then developed to ensure the interoperability among healthcare information systems successfully. AIDA has demonstrated over time the need for greater control over its agents and their activities as the need for monitoring and preventing its machines and agents. This paper presents monitoring and prevention systems that were developed for machines and agents, which allow not only prevent faults, but also watch and evaluate the behaviour of these components through monitoring dashboards. The Biomedical Multiagent Platform for Interoperability (BMaPI) implemented in Centro Hospitalar do Porto (CHP) revealed provide the necessary data and functionalities capable to manage and to monitor agents’ activities. It was found that the prevention systems identified critical situations successfully, contributing to an increase in the integrity and availability of AIDA implemented in CHP

Dancing on a Pin: Health Planning in Arizona

This publication challenges us to step back and reflect on the past, present and future of health systems. Take a deeper look at planning and how we got here, review the roles of competition and regulation, and learn about the health planning matrix along with the concept of health planning bridges. Discover for yourself if these thoughts and tools help the signal of quality health planning rise more clearly from out of the noise

Interoperability in health care

With the advancement of technology, patient information has been being computerized in order to facilitate the work of healthcare professionals and improve the quality of healthcare delivery. However, there are many heterogeneous information systems that need to communicate, sharing information and making it available when and where it is needed. To respond to this requirement the Agency for Integration, Diffusion, and Archiving of medical information (AIDA) was created, a multi-agent and service-based platform that ensures interoperability among healthcare information systems. In order to improve the performance of the platform, beyond the SWOT analysis performed, a system to prevent failures that may occur in the platform database and also in machines where the agents are executed was created. The system has been implemented in the Centro Hospitalar do Porto (one of the major Portuguese hospitals), and it is now possible to define critical workload periods of AIDA, improving high availability and load balancing. This is explored in this chapter.(undefined

The Unexpected Impact of Information-Sharing on US Pharmaceutical Supply-Chains

This paper examines the introduction of information-sharing into the supply chains for pharmaceutical products in the United States. This introduction was unusual for several reasons. First, it was catalyzed from outside the industry, by a Securities and Exchange Commission (SEC) investigation into improper financial reporting by a single manufacturer. Second, it was initiated by pharmaceutical manufacturers in order to keep distributor inventories low. Third, although its effect on pharmaceutical distributors has been profound, evidence indicates that information-sharing has had no impact on pharmaceutical manufacturers' own inventorymanagement practices.

Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

The impressive evolution of the Internet of Things and the great amount of data flowing through the systems provide us with an inspiring scenario for Big Data analytics and advantageous real-time context-aware predictions and smart decision-making. However, this requires a scalable system for constant streaming processing, also provided with the ability of decision-making and action taking based on the performed predictions. This paper aims at proposing a scalable architecture to provide real-time context-aware actions based on predictive streaming processing of data as an evolution of a previously provided event-driven service-oriented architecture which already permitted the context-aware detection and notification of relevant data. For this purpose, we have defined and implemented a microservice-based architecture which provides real-time context-aware actions based on predictive streaming processing of data. As a result, our architecture has been enhanced twofold: on the one hand, the architecture has been supplied with reliable predictions through the use of predictive analytics and complex event processing techniques, which permit the notification of relevant context-aware information ahead of time. On the other, it has been refactored towards a microservice architecture pattern, highly improving its maintenance and evolution. The architecture performance has been evaluated with an air quality case study

"Factors affecting hospital admission and recovery stay duration of in-patient motor victims in Spain"

Hospital expenses are a major cost driver of healthcare systems in Europe, with motor injuries being the leading mechanism of hospitalizations. This paper investigates the injury characteristics which explain the hospitalization of victims of traffic accidents that took place in Spain. Using a motor insurance database with 16,081 observations a generalized Tobit regression model is applied to analyse the factors that influence both the likelihood of being admitted to hospital after a motor collision and the length of hospital stay in the event of admission. The consistency of Tobit estimates relies on the normality of perturbation terms. Here a semi-parametric regression model was fitted to test the consistency of estimates, concluding that a normal distribution of errors cannot be rejected. Among other results, it was found that older men with fractures and injuries located in the head and lower torso are more likely to be hospitalized after the collision, and that they also have a longer expected length of hospital recovery stay.Body injuries, Heckit estimator, semi-parametric estimator, Hausman test JEL classification:C24, I10

Modelling hospital admission rates in São Paulo, Brazil : Lee-Carter model vs. neural networks

Mestrado Bolonha em Actuarial ScienceIn Brazil, hospital admissions represent almost 50% of the total claims cost of health insurance companies while they only represent 1% of the total medical procedures. Therefore, modeling hospital admissions is extremely useful for health insurers to assess their claim costs over time and actuaries should be capable to include that information in their analyses, in order to preserve the financial sustainability of the companies. This dissertation analyses the use of the Lee-Carter model for predicting the general level of hospital admissions in the state of São Paulo, Brazil, using the traditional ARIMA model and contrasting it with the LSTM neural network. Publicly available data between the years 2008 and 2019, divided by gender, were used. The function auto.arima from the R package forecast was used to find the best ARIMA model for the data while the LSTM neural network model was searched in a combination of 20 models, varying the learning rate and decay factor. The results showed that the LSTM model and the ARIMA have similar RMSE and MAE performance.No Brasil, hospitalizações representam quase 50% dos custos totais de sinistros em operadoras de planos de saúde enquanto representam apenas 1% dos procedimentos médicos. Portanto, estimar hospitalizações é extremamente útil para que operadoras de planos de saúde possam estimar seus custos ao longo do tempo e atuários devem ser capazes de incluir essas informações em suas análises para garantir a sustentabilidade financeira das companhias. Essa dissertação analisa o uso do modelo de Lee-Carter para prever o nível geral de hospitalizações no estado de São Paulo, Brasil, utilizando o modelo ARIMA tradicional e comparando-o com a rede neuronal LSTM. Dados públicos entre os anos de 2008 e 2019, divididos por sexo, foram utilizados. A função auto.arima do pacote R forecast foi utilizada para encontrar o melhor modelo ARIMA enquanto que a rede neuronal LSTM foi selecionada entre a combinação de 20 modelos, variando a learning rate e o decay factor. Os resultados mostraram que o modelo LSTM e o modelo ARIMA possuem RMSE e MAE similares.info:eu-repo/semantics/publishedVersio