Hospitals generate large amounts of data on a daily basis, but most of the time that data is just an overwhelming amount of information which never transitions to knowledge. Through the application of Data Mining techniques it is possible to find hidden relations or patterns among the data and convert those into knowledge that can further be used to aid in the decision-making of hospital professionals. This study aims to use information about patients with diabetes, which is a chronic (long-term) condition that occurs when the body does not produce enough or any insulin. The main purpose is to help hospitals improve their care with diabetic patients and consequently reduce readmission costs. An hospital readmission is an episode in which a patient discharged from a hospital is admitted again within a specified period of time (usually a 30 day period). This period allows hospitals to verify that their services are being performed correctly and also to verify the costs of these re-admissions. The goal of the study is to predict if a patient who suffers from diabetes will be readmitted, after being discharged, using Machine Leaning algorithms. The final results revealed that the most efficient algorithm was Random Forest with 0.898 of accuracy.

The Healthcare system is one of the most complex activity sectors, essentially due to the different kind of concepts, processes, definitions, professionals and patients. Nowadays, information plays an increasingly role in the delivery of modern health care and efficiency of health systems. In this way, computer technology can be used to improve patient safety, the quality of care provided, and workload efficiency. In clinical anesthesia practice, appropriate application of informatics promotes data standardization and integrity, and supports clinical decision-making. In order to achieve these goals, many concepts like interoperability, usability and data access should be considered when Anesthesia Information Management Systems are concerned. This article describes current issues in anesthesia information management and suggests an Electronic Process Method of Extract-TransformLoad to upgrade these systems. A list of indicators was constructed recurring to PowerBI with the objective of a better information visualization.

Abstract:Intensive care is a critical area of medicine having a multidisciplinary nature requiring all types of healthcare professionals. Given the critical environment of intensive care units (ICUs), the need to use information technologies, like decision support systems, to improve healthcare services and ICU management is evident. It is proven that unplanned and prolonged admission to the ICU is not only prejudicial to a patient's health, but also such a situation implies a readjustment of ICU resources, including beds, doctors, nurses, financial resources, among others. By discovering the common characteristics of the admitted patients, it is possible to improve these outcomes. In this study clustering techniques were applied to data collected from admitted patients in an intensive care unit. The best results presented a silhouette of 1, with a distance to centroids of 6.2 × 10 −17 and a Davies-Bouldin index of −0.652.

The processing of information in real-time (through the processing of complex events) has become an essential task for the optimal functioning of manufacturing plants. Only in this way can artificial intelligence, data extraction, and even business intelligence techniques be applied, and the data produced daily be used in a beneficent way, enhancing automation processes and improving service delivery. Therefore, professionals and researchers need a wide range of tools to extract, transform, and load data in real-time efficiently. Additionally, the same tool supports or at least facilitates the visualization of this data intuitively and interactively. The review presented in this document aims to provide an up-to-date review of the various tools available to perform these tasks. Of the selected tools, a brief description of how they work, as well as the advantages and disadvantages of their use, will be presented. Furthermore, a critical analysis of overall operation and performance will be presented. Finally, a hybrid architecture that aims to synergize all tools and technologies is presented and discussed.

PrefaceThe 13th Portuguese Conference on Artificial Intelligence, EPIA 2007, took place in the old city of Guimarães and was sponsored by the University of Minho and APPIA, the Portuguese Association for Artificial Intelligence. The city of Guimarães is classified as a UNESCO World Cultural Heritage, being located in the North of Portugal, in the Minho Region, approximately 350 Km north of the capital, Lisbon, and about 50 Km from the second largest city, the city of Oporto. Guimarães has its origin in times previous to the foundation of the Portuguese nationality, the place where Portugal was born in the twelveth century. It is proudly referred to as the Cradle of the Nation.EPIA was firstly held in the city of Oporto, in 1985. Its purpose was to promote the research in artificial intelligence (AI) and the scientific exchange among AI researchers, practitioners, scientists and engineers. The conference is devoted to all areas of artificial intelligence and covers both theoretical and foundational issues as well as applications. As in previous editions, the program was prearranged in terms of workshops dedicated to specific themes of AI, invited lectures, tutorials and sessions, all selected according to the highest standards. The program of the conference included for the first time a Doctoral Symposium.A total of 11 workshops were considered, following the guidelines of the Organizing and Advisory Committees. The list includes workshops held in the previous edition, such as GAIW, BAOSW07, IROBOT07, MASTA07, TEMA07, and ALEA, rearrangements of previous ones, such as CMBSB, and novel ones such as AIASTS, AmITA, BI07 and STCS.In this edition a total of 210 contributions were received from 29 countries,