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 lays...
| Κύριος συγγραφέας: | |
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| Συγγραφή απο Οργανισμό/Αρχή: | |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
London :
Springer London,
2011.
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| Σειρά: | Health Informatics,
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| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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.
