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

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

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)

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)