Abstract
Radiographer abnormality detection schemes (RADS) were introduced in the early 1980s to assist emergency departments. The development of PACS systems are affecting health professionals forcing them to evolve along, reviewing images on a computer monitor rather than on radiographic film. This article reviewed published articles that evaluated the impact of the use of a Red Dot System in patient outcome of emergency trauma patients and assessed the implementation of a Red Dot System in a Radiology Department with digital radiography and PACS. Few articles addressed the implementation issues and use of a Red Dot system in Computed Radiology. Radiographer skeletal red dot studies, had sensitivity and specificity of, respectively, 0.71 and 0.96 pre-training, and 0.81 and 0.95 post-training, compared with a reference standard. The use of radiographer abnormality detection schemes such as Red Dot and reporting has the potential to improve the diagnosis and outcome of emergency patients. The arrival of Information Technologies (IT) to healthcare and the introduction of Digital Radiography have limited the functionality of RADS due to incompatibility of new technology with the standard practice. New image technology solutions in Radiology should enhance the development and utilization of radiographer skills in RADS environments.

Abstract
Identity management represents an essential component for identification, authentication and authorization of patients, professionals, stakeholders and organizations in eHealth, combining information technologies and organizational procedures to provide security and privacy to health information. A literature search was conducted to identify relevant articles which were then grouped into themes according to the main subject. From the selected articles, plus their references, main findings, issues and future perspectives were systematized. A total of 31 articles were obtained, and after selection methodology 13 articles were included and grouped in four different themes: identity pseudonymisation and anonymization for secondary use, privacy preserving identity, identification, authentication and authorization identity in eHealth and identity and standardization. Through references cited in articles, research programs and working areas were also identified. Very few implementations could be found in literature, showing that this problem is even more complex than it seems and future adoption requires further research on new models and architectures. Furthermore, there is the need for a standard methodology for identity attributes interoperability between different stakeholders. Although there is a known large research effort in the context of identity in the information society in general, very few studies and experiences were found in the eHealth context.

Abstract
Cardiotocography is widely used, all over the world, for fetal heart rate and uterine contractions monitoring before (antepartum) and during (intrapartum) labor, regarding the detection of fetuses in danger of death or permanent damage. However, analysis of cardiotocogram tracings remains a large and unsolved issue. State-of-the-art monitoring systems provide quantitative parameters that are difficult to assess by the human eye. These systems also trigger alerts for changes in the behavior of the signals. However, they usually take up to 10 min to detect these different behaviors. Previous work using machine learning for concept drift detection has successfully achieved faster results in the detection of such events. Our aim is to extend the monitoring system with memory-less fading statistics, which have been successfully applied in drift detection and statistical tests, to improve detection of alarming events.

Abstract
Clinical record integration and visualization is one of the most important abilities of modern health information systems (HIS). Its use on clinical encounters plays a relevant role in the efficacy and efficiency of healthcare. However, integrated HIS of central hospitals may gather millions of clinical reports (e.g. radiology, lab results, etc.). Hence, the clinical record must manage a stream of reports being produced in the entire hospital. Moreover, not all documents from a patient are relevant for a given encounter, and therefore not visualized during that encounter. Thus, the HIS must also manage a stream of events of visualization of reports, which runs in parallel to the stream of documents production. The aim of our project is to provide the physician with a recommendation of clinical reports to consider when they log in the computer. Our approach is to model relevance as the probability that a given document will be accessed in the current time frame. For that, we design a data stream management system to process the two streams, and Bayesian networks to learn those probabilities based on document, patient, department and user information. One of the biggest challenges to the learning problem, so far, is that no negative examples are produced by the stream (i.e. there are no record of documents not being visualized) leading to a one-class classification problem. The aim of this paper is to clearly present the setting and rationale for the approach. Current work is focused on both the stream processing mechanism and the Bayesian probability estimation.

Abstract

Abstract
In recent years various methods from the field of artificial intelligence (AI) have been applied to economic problems. The subarea of multiagent systems (MAS) is particularly useful as it enables to simulate individuals or organizations and various interactions among them. In this paper we investigate a scenario with a set of agents, each belonging to a certain sector of activity (e.g. agriculture, clothing, health sector etc.). The agents produce, consume goods or services in their area of activity. Besides, our model includes also the resource of free time. The goods and resources are exchanged on a market governed by auction, which determines the prices of all goods. We discuss the problem of developing an adaptive producer that exploits reward-based learning. This facet enables the agent to exploit previous information gathered and adapt its production to the current conditions. We describe a set of experiments that show how such information can be gathered and explored in decision making. Besides, we describe a scheme that we plan to adopt in a full-fledged experiments in near future.