A standards-based ICT framework to enable a service-oriented approach to clinical decision support

This research provides evidence that standards based Clinical Decision Support (CDS) at the point of care is an essential ingredient of electronic healthcare service delivery. A Service Oriented Architecture (SOA) based solution is explored, that serves as a task management system to coordinate complex distributed and disparate IT systems, processes and resources (human and computer) to provide standards based CDS. This research offers a solution to the challenges in implementing computerised CDS such as integration with heterogeneous legacy systems. Reuse of components and services to reduce costs and save time. The benefits of a sharable CDS service that can be reused by different healthcare practitioners to provide collaborative patient care is demonstrated. This solution provides orchestration among different services by extracting data from sources like patient databases, clinical knowledge bases and evidence-based clinical guidelines (CGs) in order to facilitate multiple CDS requests coming from different healthcare settings. This architecture aims to aid users at different levels of Healthcare Delivery Organizations (HCOs) to maintain a CDS repository, along with monitoring and managing services, thus enabling transparency. The research employs the Design Science research methodology (DSRM) combined with The Open Group Architecture Framework (TOGAF), an open source group initiative for Enterprise Architecture Framework (EAF). DSRM’s iterative capability addresses the rapidly evolving nature of workflows in healthcare. This SOA based solution uses standards-based open source technologies and platforms, the latest healthcare standards by HL7 and OMG, Decision Support Service (DSS) and Retrieve, Update Locate Service (RLUS) standard. Combining business process management (BPM) technologies, business rules with SOA ensures the HCO’s capability to manage its processes. This architectural solution is evaluated by successfully implementing evidence based CGs at the point of care in areas such as; a) Diagnostics (Chronic Obstructive Disease), b) Urgent Referral (Lung Cancer), c) Genome testing and integration with CDS in screening (Lynch’s syndrome). In addition to medical care, the CDS solution can benefit organizational processes for collaborative care delivery by connecting patients, physicians and other associated members. This framework facilitates integration of different types of CDS ideal for the different healthcare processes, enabling sharable CDS capabilities within and across organizations

SNP locator: a candidate SNP selection tool

[Abstract] In this work, a data integration approach using a federated model based on a service oriented architecture (SOA) is presented. The BioMOBY middleware was used to implement each service which is part of the integration process. As an example of usage of this architecture, a web tool for candidate SNP selection has been developed. Thus, several BioMOBY services have been created as the model layer of the web application. Each data source has a wrapper which communicates with the federated model, that is, the BioMOBY model, and this model is the one that interacts with the client.Red Gallega de Investigación sobre Cáncer Colorrectal; Ref. 2009/58Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT-0366Instituto de Salud Carlos III; PIO52048Instituto de Salud Carlos III; RD07/0067/0005Galicia. Consellería de Economía e Industria ; 10SIN105004PRMinisterio de Industria, Turismo y Comercio; TSI-020110-2009-5

Novel optimization schemes for service composition in the cloud using learning automata-based matrix factorization

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of PhilosophyService Oriented Computing (SOC) provides a framework for the realization of loosely couple service oriented applications (SOA). Web services are central to the concept of SOC. They possess several benefits which are useful to SOA e.g. encapsulation, loose coupling and reusability. Using web services, an application can embed its functionalities within the business process of other applications. This is made possible through web service composition. Web services are composed to provide more complex functions for a service consumer in the form of a value added composite service. Currently, research into how web services can be composed to yield QoS (Quality of Service) optimal composite service has gathered significant attention. However, the number and services has risen thereby increasing the number of possible service combinations and also amplifying the impact of network on composite service performance. QoS-based service composition in the cloud addresses two important sub-problems; Prediction of network performance between web service nodes in the cloud, and QoS-based web service composition. We model the former problem as a prediction problem while the later problem is modelled as an NP-Hard optimization problem due to its complex, constrained and multi-objective nature. This thesis contributed to the prediction problem by presenting a novel learning automata-based non-negative matrix factorization algorithm (LANMF) for estimating end-to-end network latency of a composition in the cloud. LANMF encodes each web service node as an automaton which allows v it to estimate its network coordinate in such a way that prediction error is minimized. Experiments indicate that LANMF is more accurate than current approaches. The thesis also contributed to the QoS-based service composition problem by proposing four evolutionary algorithms; a network-aware genetic algorithm (INSGA), a K-mean based genetic algorithm (KNSGA), a multi-population particle swarm optimization algorithm (NMPSO), and a non-dominated sort fruit fly algorithm (NFOA). The algorithms adopt different evolutionary strategies coupled with LANMF method to search for low latency and QoSoptimal solutions. They also employ a unique constraint handling method used to penalize solutions that violate user specified QoS constraints. Experiments demonstrate the efficiency and scalability of the algorithms in a large scale environment. Also the algorithms outperform other evolutionary algorithms in terms of optimality and calability. In addition, the thesis contributed to QoS-based web service composition in a dynamic environment. This is motivated by the ineffectiveness of the four proposed algorithms in a dynamically hanging QoS environment such as a real world scenario. Hence, we propose a new cellular automata-based genetic algorithm (CellGA) to address the issue. Experimental results show the effectiveness of CellGA in solving QoS-based service composition in dynamic QoS environment

An SOA-based model for the integrated provisioning of cloud and grid resources

In the last years, the availability and models of use of networked computing resources within reach of e-Science are rapidly changing and see the coexistence of many disparate paradigms: high-performance computing, grid, and recently cloud. Unfortunately, none of these paradigms is recognized as the ultimate solution, and a convergence of them all should be pursued. At the same time, recent works have proposed a number of models and tools to address the growing needs and expectations in the field of e-Science. In particular, they have shown the advantages and the feasibility of modeling e-Science environments and infrastructures according to the service-oriented architecture. In this paper, we suggest a model to promote the convergence and the integration of the different computing paradigms and infrastructures for the dynamic on-demand provisioning of resources from multiple providers as a cohesive aggregate, leveraging the service-oriented architecture. In addition, we propose a design aimed at endorsing a flexible, modular, workflow-based computing model for e-Science. The model is supplemented by a working prototype implementation together with a case study in the applicative domain of bioinformatics, which is used to validate the presented approach and to carry out some performance and scalability measurements

Ambient-aware continuous care through semantic context dissemination

Background: The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e. g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate. The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data. Methods: The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability. Results: A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered. Conclusions: The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results

Are visual threats prioritized without awareness? A critical review and meta analysis involving 3 behavioral paradigms and 2696 observers

Given capacity limits, only a subset of stimuli 1 give rise to a conscious percept. Neurocognitive models suggest that humans have evolved mechanisms that operate without awareness and prioritize threatening stimuli over neutral stimuli in subsequent perception. In this meta analysis, we review evidence for this ‘standard hypothesis’ emanating from three widely used, but rather different experimental paradigms that have been used to manipulate awareness. We found a small pooled threat-bias effect in the masked visual probe paradigm, a medium effect in the binocular rivalry paradigm and highly inconsistent effects in the breaking continuous flash suppression paradigm. Substantial heterogeneity was explained by the stimulus type: the only threat stimuli that were robustly prioritized across all three paradigms were fearful faces. Meta regression revealed that anxiety may modulate threat biases, but only under specific presentation conditions. We also found that insufficiently rigorous awareness measures, inadequate control of response biases and low level confounds may undermine claims of genuine unconscious threat processing. Considering the data together, we suggest that uncritical acceptance of the standard hypothesis is premature: current behavioral evidence for threat-sensitive visual processing that operates without awareness is weak