소장자료

Metadata-driven Software Systems in Biomedicine Designing Systems that can adapt to Changing Knowledge [electronic resource] :

  • Nadkarni, Prakash M. author.
  • 2011
Metadata-driven Software Systems in Biomedicine Designing Systems that can adapt to Changing Knowledge [electronic resource] :
  • 자료유형
    단행본
  • 서명/저자사항
    Metadata-driven Software Systems in Biomedicine[electronic resource] :Designing Systems that can adapt to Changing Knowledge / by Prakash M. Nadkarni.
  • 개인저자
    Nadkarni, Prakash M., author.
  • 단체저자
    SpringerLink (Online service)
  • 형태사항
    XX, 396 p. : online resource.
  • 총서명
    Health Informatics,1431-1917
  • 내용주기
    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?셲 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: Prot챕g챕 -- 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.
  • 일반주제명
    Medicine.
    Health administration.
    Health informatics.
    Medicine & Public Health.
    Health Informatics.
    Biomedicine general.
    Health Administration.
  • 기본자료 저록
    Springer eBooks
  • 기타형태 저록
    Printed edition: 9780857295095
  • ISBN
    9780857295101
  • 언어
    영어