automated clinicalontologyextraction
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Automated Extraction of Domain-specific Clinical OntologiesSegmenting, merging, and surveying modules
Chimezie Ogbuji cut@case.edu
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)
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
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
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
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
Seidenberg and Rector (2006)
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'
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
Materials
SNOMED-CT
Foundational Model of Anatomy (FMA)
Common anatomy signature
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
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
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
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
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
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
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
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
Example
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.
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
Reifying SEP tripletsNeed 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
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
Segmenting and Merging Domain-specific Ontology Modules for Clinical Informatics (Ogbuji 2010)
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?”
Demonstration
Implementation (Python)http://code.google.com/p/python-dlp/wiki/ClinicalOntologyModules
Example: Atrial Fibrillation (disorder)
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