Accessing Patient Records in Virtual Healthcare Organisations

The ARTEMIS project is developing a semantic web service based P2P interoperability infrastructure for healthcare information systems that will allow healthcare providers to securely share patient records within virtual healthcare organisations. Authorisation decisions to access patient records across organisation boundaries can be very dynamic and must occur within a strict legislative framework. In ARTEMIS we are developing a dynamic authorisation mechanism called PBAC that provides a means of contextual and process oriented access control to enforce healthcare business processes. PBAC demonstrates how healthcare providers can dynamically share patient records for care pathways across organisation boundaries

A classification of emerging and traditional grid systems

The grid has evolved in numerous distinct phases. It started in the early ’90s as a model of metacomputing in which supercomputers share resources; subsequently, researchers added the ability to share data. This is usually referred to as the first-generation grid. By the late ’90s, researchers had outlined the framework for second-generation grids, characterized by their use of grid middleware systems to “glue” different grid technologies together. Third-generation grids originated in the early millennium when Web technology was combined with second-generation grids. As a result, the invisible grid, in which grid complexity is fully hidden through resource virtualization, started receiving attention. Subsequently, grid researchers identified the requirement for semantically rich knowledge grids, in which middleware technologies are more intelligent and autonomic. Recently, the necessity for grids to support and extend the ambient intelligence vision has emerged. In AmI, humans are surrounded by computing technologies that are unobtrusively embedded in their surroundings. However, third-generation grids’ current architecture doesn’t meet the requirements of next-generation grids (NGG) and service-oriented knowledge utility (SOKU).4 A few years ago, a group of independent experts, arranged by the European Commission, identified these shortcomings as a way to identify potential European grid research priorities for 2010 and beyond. The experts envision grid systems’ information, knowledge, and processing capabilities as a set of utility services.3 Consequently, new grid systems are emerging to materialize these visions. Here, we review emerging grids and classify them to motivate further research and help establish a solid foundation in this rapidly evolving area

Report on the EHCR (Deliverable 26.2)

This deliverable is the second for Workpackage 26. The first, submitted after Month 12, summarised the areas of research that the partners had identified as being relevant to the semantic indexing of the EHR. This second one reports progress on the key threads of work identified by the partners during the project to contribute towards semantically interoperable and processable EHRs. This report provides a set of short summaries on key topics that have emerged as important, and to which the partners are able to make strong contributions. Some of these are also being extended via two new EU Framework 6 proposals that include WP26 partners: this is also a measure of the success of this Network of Excellence

Can surgical simulation be used to train detection and classification of neural networks?

Computer-assisted interventions (CAI) aim to increase the effectiveness, precision and repeatability of procedures to improve surgical outcomes. The presence and motion of surgical tools is a key information input for CAI surgical phase recognition algorithms. Vision-based tool detection and recognition approaches are an attractive solution and can be designed to take advantage of the powerful deep learning paradigm that is rapidly advancing image recognition and classification. The challenge for such algorithms is the availability and quality of labelled data used for training. In this Letter, surgical simulation is used to train tool detection and segmentation based on deep convolutional neural networks and generative adversarial networks. The authors experiment with two network architectures for image segmentation in tool classes commonly encountered during cataract surgery. A commercially-available simulator is used to create a simulated cataract dataset for training models prior to performing transfer learning on real surgical data. To the best of authors' knowledge, this is the first attempt to train deep learning models for surgical instrument detection on simulated data while demonstrating promising results to generalise on real data. Results indicate that simulated data does have some potential for training advanced classification methods for CAI systems

Unified Theory of Relativistic Identification of Information in a Systems Age: Proposed Convergence of Unique Identification with Syntax and Semantics through Internet Protocol version 6

Unique identification of objects are helpful to the decision making process in many domains. Decisions, however, are often based on information that takes into account multiple factors. Physical objects and their unique identification may be one of many factors. In real-world scenarios, increasingly decisions are based on collective information gathered from multiple sources (or systems) and then combined to a higher level domain that may trigger a decision or action. Currently, we do not have a globally unique mechanism to identify information derived from data originating from objects and processes. Unique identification of information, hence, is an open question. In addition, information, to be of value, must be related to the context of the process. In general, contextual information is of greater relevance in the decision making process or in decision support systems. In this working paper, I shall refer to such information as decisionable information. The suggestion here is to utilize the vast potential of internet protocol version six (IPv6) to uniquely identify not only objects and processes but also relationships (semantics) and interfaces (sensors). Convergence of identification of diverse entities using the globally agreed structure of IPv6 offers the potential to identify 3.4x10[subscript 38] instances based on the fact that the 128-bit IPv6 structure can support 3.4x10[subscript 38] unique addresses. It is not necessary that all instances must be connected to the internet or routed or transmitted simply because an IP addressing scheme is suggested. This is a means for identification that will be globally unique and offers the potential to be connected or routed via the internet. In this working paper, scenarios offer [1] new revenue potential from data routing (P2P traffic track and trace) for telecommunication industries, [2] potential for use in healthcare and biomedical community, [3] scope of use in the semantic web structure by transitioning URIs used in RDF, [4] applications involving thousands of mobile ad hoc sensors (MANET) that demand dynamic adaptive auto-reconfiguration. This paper presents a confluence of ideas