3,088 research outputs found

    Designing community care systems with AUML

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    This paper describes an approach to developing an appropriate agent environment appropriate for use in community care applications. Key to its success is that software designers collaborate with environment builders to provide the levels of cooperation and support required within an integrated agent–oriented community system. Agent-oriented Unified Modeling Language (AUML) is a practical approach to the analysis, design, implementation and management of such an agent-based system, whilst providing the power and expressiveness necessary to support the specification, design and organization of a health care service. The background of an agent-based community care application to support the elderly is described. Our approach to building agent–oriented software development solutions emphasizes the importance of AUML as a fundamental initial step in producing more general agent–based architectures. This approach aims to present an effective methodology for an agent software development process using a service oriented approach, by addressing the agent decomposition, abstraction, and organization characteristics, whilst reducing its complexity by exploiting AUML’s productivity potential. </p

    Development of the Lymphoma Enterprise Architecture Database: A caBIG(tm) Silver level compliant System

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    Lymphomas are the fifth most common cancer in United States with numerous histological subtypes. Integrating existing clinical information on lymphoma patients provides a platform for understanding biological variability in presentation and treatment response and aids development of novel therapies. We developed a cancer Biomedical Informatics Grid™ (caBIG™) Silver level compliant lymphoma database, called the Lymphoma Enterprise Architecture Data-system™ (LEAD™), which integrates the pathology, pharmacy, laboratory, cancer registry, clinical trials, and clinical data from institutional databases. We utilized the Cancer Common Ontological Representation Environment Software Development Kit (caCORE SDK) provided by National Cancer Institute’s Center for Bioinformatics to establish the LEAD™ platform for data management. The caCORE SDK generated system utilizes an n-tier architecture with open Application Programming Interfaces, controlled vocabularies, and registered metadata to achieve semantic integration across multiple cancer databases. We demonstrated that the data elements and structures within LEAD™ could be used to manage clinical research data from phase 1 clinical trials, cohort studies, and registry data from the Surveillance Epidemiology and End Results database. This work provides a clear example of how semantic technologies from caBIG™ can be applied to support a wide range of clinical and research tasks, and integrate data from disparate systems into a single architecture. This illustrates the central importance of caBIG™ to the management of clinical and biological data

    The caCORE Software Development Kit: Streamlining construction of interoperable biomedical information services

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    BACKGROUND: Robust, programmatically accessible biomedical information services that syntactically and semantically interoperate with other resources are challenging to construct. Such systems require the adoption of common information models, data representations and terminology standards as well as documented application programming interfaces (APIs). The National Cancer Institute (NCI) developed the cancer common ontologic representation environment (caCORE) to provide the infrastructure necessary to achieve interoperability across the systems it develops or sponsors. The caCORE Software Development Kit (SDK) was designed to provide developers both within and outside the NCI with the tools needed to construct such interoperable software systems. RESULTS: The caCORE SDK requires a Unified Modeling Language (UML) tool to begin the development workflow with the construction of a domain information model in the form of a UML Class Diagram. Models are annotated with concepts and definitions from a description logic terminology source using the Semantic Connector component. The annotated model is registered in the Cancer Data Standards Repository (caDSR) using the UML Loader component. System software is automatically generated using the Codegen component, which produces middleware that runs on an application server. The caCORE SDK was initially tested and validated using a seven-class UML model, and has been used to generate the caCORE production system, which includes models with dozens of classes. The deployed system supports access through object-oriented APIs with consistent syntax for retrieval of any type of data object across all classes in the original UML model. The caCORE SDK is currently being used by several development teams, including by participants in the cancer biomedical informatics grid (caBIG) program, to create compatible data services. caBIG compatibility standards are based upon caCORE resources, and thus the caCORE SDK has emerged as a key enabling technology for caBIG. CONCLUSION: The caCORE SDK substantially lowers the barrier to implementing systems that are syntactically and semantically interoperable by providing workflow and automation tools that standardize and expedite modeling, development, and deployment. It has gained acceptance among developers in the caBIG program, and is expected to provide a common mechanism for creating data service nodes on the data grid that is under development

    The Use of Metamodel-based Approach for Designing Healthcare Applications

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    Recently, the use of Model-Driven Engineering (MDE) via metamodeling approach is gaining more attention for software applications development. The community from the healthcare domain also attempts to employ the metamodel approach for producing quality healthcare applications. Healthcare applications have become an imperative in every attempt to improve healthcare management. Numerous studies reported that the healthcare domain is seen as a complex and unique domain, which involves dynamic characteristics. In addition, it is widely recognized that the increase of information exchange in the healthcare domain is caused by the diversity of healthcare data. This has led to the increase use of information technologies in the healthcare industry so as to enhance the healthcare delivery process via healthcare applications. However, the complexity of healthcare information leads to ineffective models and design of healthcare applications. Modeling the healthcare processes and developing healthcare applications are challenging tasks. &nbsp;Hence, the advances of MDE have influenced the use of the metamodeling technique in the development of healthcare applications. Various metamodels are developed as a solution to provide a clear healthcare process model and a correct healthcare application. The aim of this paper is to analyse the use of the metamodel-based approach in designing healthcare applications. We believe that the metamodel-based approach would improve the development of healthcare applications.&nbsp

    Development of a Provisional Domain Model for the Nursing Process for Use within the Health Level 7 Reference Information Model

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    Objective: Since 1999, the Nursing Terminology Summits have promoted the development, evaluation, and use of reference terminology for nursing and its integration into comprehensive health care data standards. The use of such standards to represent nursing knowledge, terminology, processes, and information in electronic health records will enhance continuity of care, decision support, and the exchange of comparable patient information. As part of this activity, working groups at the 2001, 2002, and 2003 Summit Conferences examined how to represent nursing information in the Health Level 7 (HL7) Reference Information Model (RIM). Design: The working groups represented the nursing process as a dynamic sequence of phases, each containing information specific to the activities of the phase. They used Universal Modeling Language (UML) to represent this domain knowledge in models. An Activity Diagram was used to create a dynamic model of the nursing process. After creating a structural model of the information used at each stage of the nursing process, the working groups mapped that information to the HL7 RIM. They used a hierarchical structure for the organization of nursing knowledge as the basis for a hierarchical model for "Findings about the patient.” The modeling and mapping reported here were exploratory and preliminary, not exhaustive or definitive. The intent was to evaluate the feasibility of representing some types of nursing information consistently with HL7 standards. Measurements: The working groups conducted a small-scale validation by testing examples of nursing terminology against the HL7 RIM class "Observation.” Results: It was feasible to map patient information from the proposed models to the RIM class "Observation.” Examples illustrate the models and the mapping of nursing terminology to the HL7 RIM. Conclusion: It is possible to model and map nursing information into the comprehensive health care information model, the HL7 RIM. These models must evolve and undergo further validation by clinicians. The integration of nursing information, terminology, and processes in information models is a first step toward rendering nursing information machine-readable in electronic patient records and messages. An eventual practical result, after much more development, would be to create computable, structured information for nursing documentatio

    Ontology-based knowledge representation of experiment metadata in biological data mining

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    According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes

    The CAP cancer protocols – a case study of caCORE based data standards implementation to integrate with the Cancer Biomedical Informatics Grid

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    BACKGROUND: The Cancer Biomedical Informatics Grid (caBIG™) is a network of individuals and institutions, creating a world wide web of cancer research. An important aspect of this informatics effort is the development of consistent practices for data standards development, using a multi-tier approach that facilitates semantic interoperability of systems. The semantic tiers include (1) information models, (2) common data elements, and (3) controlled terminologies and ontologies. The College of American Pathologists (CAP) cancer protocols and checklists are an important reporting standard in pathology, for which no complete electronic data standard is currently available. METHODS: In this manuscript, we provide a case study of Cancer Common Ontologic Representation Environment (caCORE) data standard implementation of the CAP cancer protocols and checklists model – an existing and complex paper based standard. We illustrate the basic principles, goals and methodology for developing caBIG™ models. RESULTS: Using this example, we describe the process required to develop the model, the technologies and data standards on which the process and models are based, and the results of the modeling effort. We address difficulties we encountered and modifications to caCORE that will address these problems. In addition, we describe four ongoing development projects that will use the emerging CAP data standards to achieve integration of tissue banking and laboratory information systems. CONCLUSION: The CAP cancer checklists can be used as the basis for an electronic data standard in pathology using the caBIG™ semantic modeling methodology
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