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Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
Automated clinicalontologyextraction
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Automated clinicalontologyextraction

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Presentation of methods for segmenting, merging, and surveying domain-specific clinical ontologies in an automated fashion.

Presentation of methods for segmenting, merging, and surveying domain-specific clinical ontologies in an automated fashion.

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  • 1. Automated Extraction of Domain-specific Clinical Ontologies
    Segmenting, merging, and surveying modules
    Chimezie Ogbujicut@case.edu
  • 2. Need for Ontology Bootstrapping
    There is a critical need for formal, reproducible methods for recognizing and filling gaps in medical terminologies (Cimino 1998)
    Clinical terminology systems need to extend smoothly and quickly in response to the needs of users (Rector 1999)
    A fixed, enumerated list of concepts can never be complete and results in a combinatorial explosion of terms (exhaustive pre-coordination)
  • 3. A general best practice is to re-use ontologies, especially those that have been standardized
    However, there is a proliferation of (domain-specific) clinical ontologies
    Flies in the face of this best practice
    As more projects leverage the full value of reference, medical ontologies, there will be an increased need for automated management:
    Not there yet, mostly have coding systems
  • 4. The Goal
    Want to (automatically)
    Customize a large source ontology such as SNOMED-CT in a tractable way
    Generate normalized, anatomy and clinical terminology modules that are manageable in size, and preserve the meaning of common terms
    Provide a framework for bootstrapping the creation of clinical terminology for a specific domain
  • 5. Prior Work
    Noy and Musen (2000)
    Discuss how to either automate the merging and alignment or guide the user, suggesting conflicts and actions to take
    Rely on lexical matching of term names
    Bontas and Tolksdorf (2005)
    Similar goal as Noy & Musen
    User provides a list of term matches between source & target
    Follow semantic connections from these terms
  • 6. Modularization:Ontology Engineering
    Seidenberg and Rector (2006) describe an ontology segmentation heuristic that starts with a set of terms and creates an extract from an ontology around those terms
    Traverses ontology structure and is limited by user-specified recursion depth
  • 7. Seidenberg and Rector (2006)
  • 8. Grau et al. (2008): Developing ontology P and want to re-use a set of symbols from (another) ontology Q without changing their meaning
    P + Q is a conservative extension of Q
    When answering a query involving terms in O (its signature or vocabulary), importing O'1 should give the same answers as if O' had been imported instead (both are subsets but O'1 is more manageable):
    Then we say O'1 is a module for O in O'
  • 9. Segments v.s. Modules
    The segmentation heuristic used is in contrast to (and predates) those of Grau et al. (2008) that produce modules with 100% semantic fidelity
    Sacrifice semantic fidelity for an expedient extraction process
    The (tractable) calculation of deductive, conservative extensions for EL is an open research problem
  • 10. Materials
    SNOMED-CT
    Foundational Model of Anatomy (FMA)
    Common anatomy signature
  • 11. Reference Clinical Ontologies
    There is a reasonable consensus around two reference ontologies that cover a substantial portion of clinical medicine
    SNOMED-CT and the FMA
    Both leverage an underlying formal knowledge representation
  • 12. SNOMED-CT
    A comprehensive terminological framework for clinical documentation and reporting.
    Comprised of about half a million concepts:
    Clinical findings, procedures, body structures, organisms, substances, pharmaceutical products, specimen, quantitative measures, and clinical situations
    Has an underlying description logic (EL family)
    EL family has shown to be suitable for medical terminology
    And subsequently, ELHR+, the performance target of many modern classifiers
  • 13. Technical challenges:
    Its size discourages the use of logical inference systems to manage and process it (due to performance issues)
    Most description logic systems run into challenges with memory exhaustion when classifying it in its entirety (there have been recent advances here)
    In some cases, its definitions are inconsistent or incomplete (more on this later)
    Policy pressures (opportunity):
    Participants in meaningful use program must capture EHR problem lists based on ICD-9 or SNOMED-CT
  • 14. Using Modulzarization for Quality Assurance
    Plenty of (recent) work on quality assurance of SNOMED-CT
    Using Semantic Web technologies (and lattice theory) for quality assurance of large biomedical ontologies (Zhang et al. 2010)
    Identifying incorrect or clinically misleading SNOMED-CT inferences that arose from use of SNOMED-CT(Rector et al. 2011)
    More, recent QA of SNOMED-CT (Rector 2011) leverages extraction of manageable modules and discusses the value to domain experts of browsing SNOMED-CT via a module built from a set of terms relevant to a domain or application
  • 15. Foundational Model of Anatomy
    Goal is to conceptualize the physical objects and spaces that constitute the human body
    Leverages a frame-based knowledge representation to formulate over 75,000 concepts including:
    Macroscopic, microscopic, and sub-cellular canonical anatomy
    Anatomy is fundamental to biomedical domains
  • 16. Concepts are connected by several mereological relations
    Primarily concerned with part_of and has_part
    Adheres to a strict, aristotelian modeling paradigm
    Ensures definitions are consistent and state the essence of anatomy in terms of their characteristics
    Using July 24th 2008 ALPHA version of the FMA 2.0 in OBO foundry
  • 17. Common Anatomy Signature
    There is a significant overlap between anatomy terms in SNOMED-CT and FMA
    Bodenreider and Zhang (2006) analyzed this overlap
    Leveraged lexical and structural analysis
    Identified ~ 7500 common concepts
    Refer to as Sanatomy
  • 18. Small Detail: SEP Triplets
    SNOMED-CT uses SEP triplets to model anatomy concepts and their relationships to each other
    For every proper SNOMED-CT anatomy concept (an Entire class), there are two auxiliary classes:
    A Structure class
    A Part class
  • 19. Example
  • 20. Main motivation is to rely on subsumption to reason about part-whole relationships
    SNOMED-CT is moving away from this, but for the purpose of using it in concert with the FMA, this is still an issue
    Previous work (Suntisrivaraporn 2007) demonstrated how an expressive description logic can be used to more directly represent mereological relations.
  • 21. Build on this but re-use terms (a transliteration) from a reference ontology of anatomy rather than re-using SNOMED-CT terms
    To preserve the meaning of anatomy terms but increase the (latent) knowledge about them and provide a terminology path to additional terms of interest
  • 22. Reifying SEP triplets
    Need to replace SNOMED-CT anatomy terms in a way that preserves the intent of the SEP anatomy scheme
    Transcribe them into a more expressive description logic
    Define a set of rules to determine how axioms involving mapped SNOMED-CT terms are replaced
    (Shultz et al. 1998) describe how to logically identify components of an SEP triplet
  • 23.
  • 24. Method
    Start with a list of user-specified SNOMED-CT concepts
    Determines the domain
    3 step process resulting in
    A SNOMED-CT module: O'snct-fma
    Transliteration of SEP triplets
    FMA segment: O'fma-snct
    Directly merge results into a single ontology
  • 25. Segmenting and Merging Domain-specific Ontology Modules for Clinical Informatics (Ogbuji 2010)
  • 26.
  • 27. Collecting the domain of discourse
    (Sahoo et al. 2011) Automatically extract a minimal common set of terms (upper-domain ontology) from an existing domain ontology
    Can be used to survey the generation of anatomy and clinical terminology modules:
    “For a given domain, what are the most general categories of (clinical) terminology that can be automatically extracted from specific distributions of SNOMED-CT and the FMA?”
  • 28. Demonstration
    Implementation (Python)
    http://code.google.com/p/python-dlp/wiki/ClinicalOntologyModules
    Example: Atrial Fibrillation (disorder)

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