Ebook: From Grid to Healthgrid

We describe a prototype information broker that has been developed to address typical healthcare information needs, using web services to obtain data from autonomous, heterogeneous sources. Some key features are reviewed: how data sources are turned into data services; how we enforce a distributed access control policy; and how semantic interoperability is achieved between the broker and its data services. Finally, we discuss the role that such a broker might have in a Grid context, as well as the limitations this reveals in current Grid provision.

Bioinformatics applications are often characterized by a combination of (pre) processing of raw data representing biological elements, (e.g. sequence alignment, structure prediction), and an high level data mining analysis. Developing such applications needs knowledge of both data mining and bioinformatics domains, that can be effectively achieved by combining ontology about the application domain and ontology about the approaches and processes to solve the given problem. In this paper we talk about using ontologies to model proteomics in silico experiments. In particular data mining of mass spectrometry proteomics data is considered.

This paper describes a sub‐project of BioGrid project called “HTC (High Throughput Computing) group.” Generally, a protein structure prediction which requires large amount of computational resources is done by trial‐and‐error method. HTC group have been developing a high throughput computing system with a flexible workflow handling mechanism for a protein structure prediction. In this paper, we show how to apply our high throughput computing system to the protein structure predictor called “ROKKY.”

New experimental technologies have rapidly transformed biomedical research into a data‐intensive discipline. Being grafted onto Grid environment ontologies deliver an effective “onto‐technology” to explore wide variety of different sorts of links in heterogeneous distributed data sources as well as to define new facts and represent new relationships between data sets.

Healthgrids unite a large amount of independent and distributed organisations to provide for various healthcare services. Often the involved organisations can belong to different areas of healthcare and even different countries. However to achieve efficient operation they have to act in a well coordinated manner. As a result, an efficient knowledge sharing between multiple participating parties of the healthgrid is required. The paper describes application of an earlier developed ontology‐driven KSNet (Knowledge Source Network) – approach to knowledge repository support for healthgrids. This approach is based on representation of knowledge via ontologies using formalism of object‐oriented constraint networks. Such representation makes it possible to define and solve various tasks from the areas of management, planning, configuration, etc., by using constraint solving engines such as, for instance, ILOG or CLP. The major discussed aspects cover the formalism of knowledge representation via ontologies and implementation of the approach as a decision support system for a case study from the area of health service logistics.

The MammoGrid project has deployed its Service‐Oriented Architec‐ture (SOA)‐based Grid application in a real environment comprising actual participating hospitals. The resultant setup is currently being exploited to conduct rigorous in‐house tests in the first phase before handing over the setup to the actual clinicians to get their feedback. This paper elaborates the deployment details and the experiences acquired during this phase of the project. Finally the strategy regarding migration to an upcoming middleware from EGEE project will be described. This paper concludes by highlighting some of the potential areas of future work.

Leveraging the advances of today's commodity graphics hardware, adoption of community proven collaboration technology, and the use of standard Web and Grid technologies a flexible system is designed to enable the construction of a distributed collaborative radiological visualization application. The system builds from a prototype application as well as requirements gathered from users. Finally constraints on the system are evaluated to complete the design process.

This paper presents an architecture defined for searching and executing Clinical Decision Support Systems (CDSS) in a LCG2/GT2 [1,2] Grid environment, using web‐based protocols. A CDSS is a system that provides a classification of the patient illness according to the knowledge extracted from clinical practice and using the patient's information in a structured format. The CDSS classification engines can be installed in any site and can be used by different medical users from a Virtual Organization (VO) [3]. All users in a VO can consult and execute different classification engines that have been installed in the Grid independently of the platform, architecture or site where the engines are installed or the users are located. The present paper present a solution to requirements such as short‐job execution, reducing the response delay on LCG2 environments and providing grid‐enabled authenticated access through web portals. Resource discovering and job submission is performed through web services, which are also described in the article.

Current advances in biosensor technology allow multiple miniaturized or textile sensors to record continuously biosignals, such as blood pressure or heart rate, and transmit the information of interest to clinical sites. New applications are emerging, based on such systems, towards pervasive healthcare. This paper describes an architecture enabling biosensors, forming a Body Area Network (BAN), to be integrated in a Grid infrastructure. The Grid services proposed, such as access to recorded data, are offered via the BAN console, an enhanced wearable computer, where the recordings of multiple biosensors are integrated. Medical Grid‐enabled Nodes can have access to biosensor measurements upon demand, or can agree to get notifications and alerts. Thus, in such a distributed environment, data and computational resources are independent, yet cooperating unobtrusively, contributing to the notion of pervasive healthcare.

Through support from the National Institutes of Health's National Center for Research Resources, the Biomedical Informatics Research Network (BIRN) is pioneering the use of advanced cyberinfrastructure for medical research. By synchronizing developments in advanced wide area networking, distributed computing, distributed database federation, and other emerging capabilities of e‐science, the BIRN has created a collaborative environment that is paving the way for biomedical research and clinical information management. The BIRN Coordinating Center (BIRN‐CC) is orchestrating the development and deployment of key infrastructure components for immediate and long‐range support of biomedical and clinical research being pursued by domain scientists in three neuroimaging test beds.

In this paper we describe the ProGenGrid (Proteomics and Genomics Grid) system, developed at the CACT/ISUFI of the University of Lecce which aims at providing a virtual laboratory where e‐scientists can simulate biological experiments, composing existing analysis and visualization tools, monitoring their execution, storing the intermediate and final output and finally, if needed, saving the model of the experiment for updating or reproducing it. The tools that we are considering are software components wrapped as Web Services and composed through a workflow. Since bioinformatics applications need to use high performance machines or a high number of workstations to reduce the computational time, we are exploiting a Grid infrastructure for interconnecting wide‐spread tools and hardware resources. As an example, we are considering some algorithms and tools needed for drug design, providing them as services, through easy to use interfaces such as the Web and Web service interfaces built using the open source gSOAP Toolkit, whereas as Grid middleware we are using the Globus Toolkit 3.2, exploiting some protocols such as GSI and GridFTP.

Grid technology can provide medical organisations with powerful tools through which they can gain coordinated access to computational resources that hitherto where inaccessible to them. This paper discusses how several classes of medical applications could benefit from the use of Grid technology. We concentrate on applications that were put forward by partners in the Dutch VL‐e project. After describing the difficulties related to the realization of such applications without making use of the Grid, we describe an architecture that allows the applications to use Grid resources. We demonstrate how this architecture can be integrated into existing systems to provide flexible and transparent access to Grid services and show performance results of a test case.

The computational requirements in Neurophysiology are increasing with the development of new analysis methods. The resources the GRID has to offer are ideally suited for this complex processing. A practical implementation of the GRID, Condor, has been assessed using a local cluster of 920 PCs. The reduction in processing time was assessed in spike recognition of the Electroencephalogram (EEG) in epilepsy using wavelets and the computationally demanding task of non‐linear image reconstruction with Electrical Impedance Tomography (EIT). Processing times were decreased by 25 and 40 times respectively. This represents a substantial improvement in processing time, but is still sub optimal due to factors such as shared access to resources and lack of checkpoints so that interrupted jobs had to be restarted. Future work will be to use these methods in non‐linear EIT image reconstruction of brain function and methods for automated EEG analysis, if possible with further optimized GRID middleware.

In this work we present a Grid‐based optimization approach performed on a set of parameters that affects both the geometric and grey‐level appearance properties of a three‐dimensional model‐based algorithm for cardiac MRI segmentation. The search for optimal values was assessed by a Monte Carlo procedure using computational Grid technology. A series of segmentation runs were conducted on an evaluation database comprising 30 studies at two phases of the cardiac cycle (60 datasets), using three shape models constructed by different methods. For each of these model‐patient combinations, six parameters were optimized in two steps: those which affect the grey‐level properties of the algorithm first and those relating to the geometrical properties, secondly. Two post‐processing tasks (one for each stage) collected and processed (in total) more than 70000 retrieved result files. Qualitative and quantitative validation of the fitting results indicates that the segmentation performance was greatly improved with the tuning. Based on the experienced benefits with the use of our middleware, and foreseeing the advent of large‐scale tests and applications in cardiovascular imaging, we strongly believe that the use of Grid computing technology in medical image analysis constitutes a real necessity.

The main purpose of the MAGIC‐5 collaboration is the development of Computer Aided Detection (CAD) software for Medical Applications on distributed databases by means of a GRID Infrastructure Connection. A prototype of the system, based on the AliEn GRID Services is already available with a central Server running common services and several clients connecting to it. It has been already successfully used for applications in mammography together with a specific CAD developed within the collaboration. Applications to the case of malignant nodule detection in lung CT scans are now being implemented, while a use of the GRID services is also being applied to PET image analysis aiming at early Alzheimer disease. One of the future prospect of our project is the migration from AliEn to the EGEE/gLite middleware which is likely to become a European standard and will certainly provide more sophisticated tools with respect to the present AliEn functionality. In this work the status of the project and its future prospects will be given, with particular attention to the data management and processing aspects. Medical applications carried on by the collaboration will be also described together with the analysis of the results so far obtained.

Mammals adapt to a rapidly changing world because of the sophisticated perceptual and cognitive function enabled by the neocortex. The neocortex, which has expanded to constitute nearly 80% of the human brain seems to have arisen from repeated duplication of a stereotypical template of neurons and synaptic circuits with subtle specializations in different brain regions and species. Determining the design and function of this microcircuitry is therefore of paramount importance to understanding normal and abnormal higher brain function. Recent advances in recording synaptically‐coupled neurons has allowed rapid dissection of the neocortical microcircuitry thus yielding a massive amount of quantitative anatomical, electrical and gene expression data on the neurons and the synaptic circuits that connect the neurons. Due to the availability of the above mentioned data, it has now become imperative to database the neurons of the microcircuit and their synaptic connections. The NEOBASE project, aims to archive the neocortical microcircuit data in a manner that facilitates development of advanced data mining applications, statistical and bioinformatics analyses tools, custom microcircuit builders, and visualization and simulation applications. The database architecture is based on ROOT, a software environment that allows the construction of an object oriented database with numerous relational capabilities. The proposed architecture allows construction of a database that closely mimics the architecture of the real microcircuit, which facilitates the interface with virtually any application, allows for data format evolution, and aims for full interoperability with other databases. NEOBASE will provide an important resource and research tool for studying the microcircuit basis of normal and abnormal neocortical function. The database will be available to local as well as remote users using Grid based tools and technologies.

The ARTEMIS project is developing a semantic web service based P2P interoperability infrastructure for healthcare information systems. The strict legislative framework in which these systems are deployed means that the interoperability of security and privacy mechanisms is an important requirement in supporting communication of electronic healthcare records across organisation boundaries. In ARTEMIS, healthcare providers define semantically annotated security and privacy policies for web services based on organisational requirements. The ARTEMIS mediator uses these semantic web service descriptions to broker between organisational policies by reasoning over security and clinical concept ontologies.

GEMSS (Grid Enabled Medical Simulation Services IST‐2001‐37153) is an EU project funded to provide a test bed for Grid‐enabled health applications. Its purpose is evaluation of Grid computing in the health sector. The health context imposes particular constraints on Grid infrastructure design, and it is this that has driven the feature set of the middleware. In addition to security, the time critical nature of health applications is accommodated by a Quality of Service component, and support for a well defined business model is also included. This paper documents experience of a GEMSS compliant radiosurgery application running within the Medical Physics department at the Royal Hallamshire Hospital in the UK. An outline of the Grid‐enabled RAPT radiosurgery application is presented and preliminary experience of its use in the hospital environment is reported. The performance of the software is compared against GammaPlan (an industry standard) and advantages/disadvantages are highlighted. The RAPT software relies on features of the GEMSS middleware that are integral to the success of this application, and together they provide a glimpse of an enabling technology that can impact upon patient management in the 21^st century.

This paper reports on our experiences of being involved in requirements capture for a HealthGrid project. Large scale, collaborative projects with multiple partners tend to experience numerous problems in the requirements capture phase (and often beyond) and HealthGrid projects are no exception. Projects with highly innovative objectives often have additional sets of problematics, however. In carving out new visions of, for example, clinical research and healthcare service delivery, HealthGrid projects have to reckon with – and work within – existing healthcare policy, legislative frameworks, professional cultures and organisational politics as well as the more common integration probkem of dealing with legacy systems. Such factors are not conducive to the achievement in healthcare of the e‐Science vision of seamless integration of information and collaborative working across administrative, professional and organisational boundaries. In this paper, we document some of the challenges we encountered in investigating the requirements for eDiaMoND, a flagship pilot UK e‐Science project. We discuss what we might learn from these challenges, especially approaches to requirements capture that are appropriate for projects with innovative aims and are also sensitive to representing and addressing what may be complex professional and organisational interests.

This paper identifies issues which will need to be addressed in pursuing the aims and objectives of the European Classification of Infertility Taskforce (ECIT), namely: to establish classification codes for infertility management; to improve the consistency of infertility information collection by specialist centres, particularly but not exclusively by computerised systems; to use these codes to enable the transfer of infertility information from specialist centres to national infertility data registries; to develop a Grid linking the data held in European infertility data registries; to use Grid processing to mine the data in the European infertility data registries to optimise patient management improving the effectiveness of treatment and reducing the risk.