Why decision support systems are important for medical education

During the last decades the inclusion of digital tools in health education has rapidly lead to a continuously enlarging digital era. All the online interactions between learners and tutors, the description, creation, reuse and sharing of educational digital resources and the interlinkage between them in conjunction with cheap storage technology has led to an enormous amount of educational data. Medical education is a unique type of education due to accuracy of information needed, continuous changing competences required and alternative methods of education used. Nowadays medical education standards provide the ground for organizing the educational data and the paradata. Analysis of such education data through education data mining techniques is in its infancy, but decision support systems for medical education need further research. To the best of our knowledge, there is a gap and a clear need for identifying the challenges for decision support systems in medical education in the era of medical education standards. Thus, in this paper the role and the attributes of such a decision support system for medical education are delineated and the challenges and vision for future actions are identified

Technical evaluation of the mEducator 3.0 linked data-based environment for sharing medical educational resources

mEducator 3.0 is a content sharing approach for medical education, based on Linked Data principles. Through standardization, it enables sharing and discovery of medical information. Overall the mEducator project seeks to address the following two different approaches, mEducator 2.0, based on web 2.0 and ad-hoc Application Programmers Interfaces (APIs), and mEducator 3.0, which builds upon a collection of Semantic Web Services that federate existing sources of medical and Technology Enhanced Learning (TEL) data. The semantic mEducator 3.0 approach It has a number of different instantiations, allowing flexibility and choice. At present these comprise of a standalone social web-based instantiation (MetaMorphosis+) and instantiations integrated with Drupal, Moodle and OpenLabyrinth systems. This paper presents the evaluation results of the mEducator 3.0 Linked Data based environment for sharing medical educational resources and focuses on metadata enrichment, conformance to the requirements and technical performance (of the MetaMorphosis+ and Drupal instantiations)

Coronary MR angiography at 3T: fat suppression versus water-fat separation

Objectives: To compare Dixon water-fat suppression with spectral pre-saturation with inversion recovery (SPIR) at 3T for coronary magnetic resonance angiography (MRA) and to demonstrate the feasibility of fat suppressed coronary MRA at 3T without administration of a contrast agent. Materials and methods: Coronary MRA with Dixon water-fat separation or with SPIR fat suppression was compared on a 3T scanner equipped with a 32-channel cardiac receiver coil. Eight healthy volunteers were examined. Contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), right coronary artery (RCA), and left anterior descending (LAD) coronary artery sharpness and length were measured and statistically compared. Two experienced cardiologists graded the visual image quality of reformatted Dixon and SPIR images (1: poor quality to 5: excellent quality). Results: Coronary MRA images in healthy volunteers showed improved contrast with the Dixon technique compared to SPIR (CNR blood-fat: Dixon = 14.9 ± 2.9 and SPIR = 13.9 ± 2.1; p = 0.08, CNR blood-myocardium: Dixon = 10.2 ± 2.7 and SPIR = 9.11 ± 2.6; p = 0.1). The Dixon method led to similar fat suppression (fat SNR with Dixon: 2.1 ± 0.5 vs. SPIR: 2.4 ± 1.2, p = 0.3), but resulted in significantly increased SNR of blood (blood SNR with Dixon: 19.9 ± 4.5 vs. SPIR: 15.5 ± 3.1, p < 0.05). This means the residual fat signal is slightly lower with the Dixon compared to the SIPR technique (although not significant), while the SNR of blood is significantly higher with the Dixon technique. Vessel sharpness of the RCA was similar for Dixon and SPIR (57 ± 7 % vs. 56 ± 9 %, p = 0.2), while the RCA visualized vessel length was increased compared to SPIR fat suppression (107 ± 21 vs. 101 ± 21 mm, p < 0.001). For the LAD, vessel sharpness (50 ± 13 % vs. 50 ± 7 %, p = 0.4) and vessel length (92 ± 46 vs. 90 ± 47 mm, p = 0.4) were similar with both techniques. Consequently, the Dixon technique resulted in an improved visual score of the coronary arteries in the water fat separated images of healthy subjects (RCA: 4.6 ± 0.5 vs. 4.1 ± 0.7, p = 0.01, LAD: 4.1 ± 0.7 vs. 3.5 ± 0.8, p = 0.007). Conclusions: Dixon water-fat separation can significantly improve coronary artery image quality without the use of a contrast agent at 3T

Real-Time, Real World Learning—Capitalising on Mobile Technology

This chapter explores the adoption of Web 2.0 technologies to promote active learning by students and to both mediate and enhance classroom instruction. Web 2.0 refers to open source, web-enabled applications (apps) that are driven by user-manipulated and user-generated content (Kassens-Noor, 2012). These apps are often rich in user participation, have dynamic content, and harness the collective intelligence of users (Chen, Hwang, & Wang, 2012). As such, these processes create “active, context based, personalised learning experiences” (Kaldoudi, Konstantinidis, & Bamidis, 2010, p. 130) that prioritise learning ahead of teaching. By putting the learner at the centre of the education process educators can provide environments that enhance employability prospects and spark a passion for learning that, hopefully, lasts a lifetime. As such, we critique an active learning approach that makes use of technology such as mobile applications (apps), Twitter, and augmented reality to enhance students’ real world learning. Dunlap and Lowenthal (2009) argue that social media can facilitate active learning as they recreate informal, free-flowing communications that allow students and academics to connect on a more emotional level. Furthermore, their use upskills students in the technical complexities of the digital world and also the specialised discourses that are associated with online participation, suitable for real world learning and working (Fig. 16.1). Three case studies explore the benefits of Web 2.0 processes. The first details the use of Twitter chats to connect students, academics, and industry professionals via online synchronous discussions that offer a number of benefits such as encouraging concise writing from students and maintaining on-going relationships between staff, students, and industry contacts. The second details a location-based mobile app that delivers content to students when they enter a defined geographical boundary linked to an area of a sports precinct. Finally, we explore the use of augmented reality apps to enhance teaching in Human Geography and Urban Studies

An Environment Supporting Visual Information Processing Services

Visual information processing is a rather mature and rich field. An impressive amount of image and video processing algorithms are currently available for a great variety of applications. There is also a considerable number of software packages, either commercial products or academic shareware, ranging from dedicated systems aiming at solving very specific problems, to open environments designed for research and development purposes. However, there is a lack of a common framework that integrates all prior efforts and developments in the field, and at the same time provides added-value features that support and in essence realize a `processing service&apos; for networked visual information systems. This report discusses visual information processing services and presents a distributed, autonomous, cooperating agent architecture, which has been used to design and implement DIPE, a novel environment for the support of such services. An Environment Supporting Visual Information Processing Se..

DIPE: A Distributed Environment for Medical Image Processing

. DIPE is a distributed environment that provides image processing services over integrated teleradiology services networks. DIPE integrates existing and new image processing software and employs sophisticated execution scheduling mechanisms for the efficient management of computational resources within a distributed environment. It can also be extended to provide various addedvalue services, such as management and retrieval of image processing software modules, as well as advanced charging procedures based on quality of service. DIPE can be viewed as the natural evolution of the legacy field of medical image processing towards a service over the emergent health care telematics networks. 1. Introduction In recent years, advances in information technology and telecommunications have acted as catalysts for significant developments in the sector of health care. These technological advances have had a particularly strong impact in the field of medical imaging, where film radiographic tech..