Metadata-driven Software Systems in Biomedicine Designing Systems that can adapt to Changing Knowledge /

To build good systems, one needs both good development skills as well as a thorough knowledge of the problem one is trying to solve. Knowledge of software history - what has worked and what hasn't - also helps in these types of detailed projects. Metadata-Driven Software Systems in Biomedicine...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Nadkarni, Prakash M. (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Springer London : Imprint: Springer, 2011.
Edición:1st ed. 2011.
Colección:Health Informatics,
Temas:
Medical informatics.
Medicine-Research.
Biology-Research.
Public health administration.
Health Informatics.
Biomedical Research.
Health Administration.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 1. What is metadata? Types of metadata
  • Descriptive (interpreted by humans)
  • Technical (utilized by software)
  • Some metadata shows characteristics of both
  • How metadata is represented
  • Why use metadata to build biomedical systems? Caveat: Metadata-driven systems are initially harder to build, Building for change: flexibility and maintainability, Elimination of repetitious coding tasks, Case Study: Table-driven approaches to software design
  • 2. Metadata for supporting electronic medical records
  • The Entity-Attribute-Value (EAV) data model:
  • Why EAV is problematic without metadata-editing capabilities: the TMR experience
  • Pros and Cons of EAV: When not to use EAV
  • How metadata allows ad hoc query to be data-model agnostic
  • Transactional operations vs. warehousing operations
  • Case Study: The I2B2 clinical data warehouse model
  • Providing end-user customizability, Case Study: EpicCare Flowsheets
  • 3. Metadata for clinical study data management systems (CSDMS)
  • Critical differences between an EMR and a CSDMS
  • Essential elements of a CSDMS
  • HTML-based vs. non-Web interfaces: pros and cons
  • Case Study: Metadata for robust interactive data validation
  • Metadata and the support of basic bioscience research
  • Object dictionaries and synonyms: the NCBI Entrez approach
  • Fundamentals of object-oriented modeling: the use of classes
  • Case study: representing neuroscience data: SenseLab
  • Case study: managing phenotype data
  • 4. Descriptive Metadata: Controlled Biomedical Terminologies
  • Classification of Controlled Vocabularies, with examples: Collections of Terms, Taxonomies: a hierarchical structure, Thesauri: Concepts vs. Terms, Ontologies: Classes and Properties, Cimino's criteria for a good controlled vocabulary, Fundamentals of Description Logics, Pre-coordination vs. compositional approaches to new concept definition, Challenges when the set of permissible operations is incomplete, Difficulties in end-user employment of large vocabularies, The use of vocabulary subsets: the 95/5 problem, Case Study: the SNOMED vocabulary
  • 5. Metadata and XML
  • Introduction to XML
  • Strengths of XML for information interchange
  • Misconceptions and common pitfalls in XML use
  • Weaknesses of XML as the basis for data modeling
  • The Microarray Gene Expression Data (MGED) experience
  • Use of the Unified Modeling Language
  • UML is intended for human visualization
  • UML has an internal XML equivalent (XMI)
  • Case Study: Clinical text markup
  • 6. Metadata and the modeling of ontologies
  • Ontology modeling tools: Protégé
  • Common Pitfalls in Ontology Modeling
  • Scalable ontology designs
  • Supporting reasoning in ontologies: classification
  • An introduction to Semantic Web technologies
  • Limitations: the open-world assumption
  • Case Study: Implementing constraints in SNOMED
  • 7. Metadata and Production-Rule Engines
  • Introduction to Production-Rule Systems
  • Strengths and weaknesses of rule frameworks
  • Embedded rule engines
  • Data that can be executed as code: the Eval function
  • Designing for extensibility
  • Supporting versioning
  • Case Study: The Jones Criteria for Rheumatic Fever
  • 8. Biomedical Metadata Standards
  • Why there can be no universal standard: a metadata model is problem-specific
  • Standards for Descriptive Metadata
  • ISO/IEC 11179: Purpose and Limitations
  • Standards for Technical Metadata
  • Have been designed for individual problem domains
  • CDISC for clinical study data interchange
  • Interchange standards for gene expression and proteomics
  • 9. The HL7 v3 Reference Information Model
  • Elements of the model
  • What the model is not intended to encompass
  • The clinical document architecture
  • The Messaging Standard: Backward Incompatibilities
  • Limitations and controversies.

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