A Study of Semantic Proximity between Archetype Terms based on SNOMED CT Relationships

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BackgroundObjectives

Semantic proximity in archetypesMapping archetypes to SNOMED

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A Study of Semantic Proximity between ArchetypeTerms based on SNOMED CT Relationships

J.L.Allones, D.Penas, M.Taboada, D.Martinez and S.Tellado

KEAM Research Group

J.L. Allones, D. Penas, M. Taboada, D. Martinez et al. A Study of Semantic Proximity between Archetype Terms based on SNOMED CT Relationships

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.. Outline

...1 BackgroundOpenEHR archetypesSNOMED CTRelated Work

...2 Objectives

...3 Semantic proximity in archetypesDatasetMethodsResultsConclusion

...4 Mapping archetypes to SNOMEDMethodsEvaluationResultsDiscussion


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OpenEHR archetypesSNOMED CTRelated Work

.. Outline


...2 Objectives




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.. OpenEHR archetypes

.What is openEHR?..

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Open standard specification that describes the management,storage and exchange of clinical data in EHR.

.What are the archetypes?..

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OpenEHR defines clinical data models called archetypes.They model the clinical information required to record particularclinical statements, such as tobacco use and exposure.


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Extract of the archetype tobacco:


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.Current status of archetypes..

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Important institutions have participated in thedevelopment of archetypes.

Problem: Archetypes hardly contain mappingsto standard concepts


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.. OpenEHR archetypes: Need for clinical terminologies

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Terminologies are required:

To capture, use and transfer clinical data in astandard form.

To reuse information collected in the course ofpatient care.

Challenge of health informatics:

To integrate archetypes and terminologies.


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.. SNOMED CT

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SNOMED CT is the best positioned terminologyto semantically annotate archetypes because it isan international standard which provides a consistentterminology across all health care domains.


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.. SNOMED CT

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Features:

Over 300,000 medical concepts.

Concepts can have several descriptions and semanticrelationships to other concepts.


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.. SNOMED CT.

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IS A relationships are also known as ”Supertype -Subtype relationships”. IS A relationships are thebasis of SNOMED CT’s hierarchies.


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.. SNOMED CT

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Attribute relationships associate two concepts speci-fying a defining characteristic of one of the concepts.


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.Need for automated mapping..

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SNOMED is a huge terminology↓

Manual annotation of archetypes with SNOMEDconcepts is very time-consuming.

↓Automated mapping methods are needed.


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.. Related Work

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Some studies have used lexical and linguistictechniques to compare the strings of archetypefragments and SNOMED CT concepts.

The works have not achieved optimum results:They map correctly about 60% of archetype fragments.

They obtain too many candidate concepts for eachfragment (many of them are not relevant).


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.. Related Work

.Difficulties in automated mapping..

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...1 Modelling archetypes separately from SNOMEDleads to differences between them: disparity indocumentation granularity or naming differences

Example of naming differences:


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.. Related Work

.Difficulties in automated mapping..

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...2 Some archetype terms have not been explicitlymodelled in the archetypes.

Example:


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There are plenty of situations in which archetype clinicalinformation is semantically related

Similarities between the structure of the archetypes and thenetwork of SNOMED relationships

↓This may help in the automated mapping


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.. Outline


...2 Objectives




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.. Objectives

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...1 To understand better how archetype clinicalinformation is semantically related.

...2 To evaluate whether a combination of contextand structure-based techniques with lexical andlinguistic techniques can improve the automatedmapping of archetypes.


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DatasetMethodsResultsConclusion

.. Outline


...2 Objectives




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.. Studying semantic proximity between archetype terms

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We checked the frequency and type of semanticrelationships between data fragments of 25archetypes.

SNOMED relationships are the reference.


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.. Dataset: 25 Observation archetpes

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25 OBSERVATION archetypes of the NHS repository.

These archetypes record information about the condi-tion of patients, such as the measurement of heart rateand tobacco use.


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.. Parsing archetypes

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A parser was used to preserve data fragments with clinical meaning.

Parser gets a dependency tree of data fragments for eacharchetype.


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.. Parsing archetypes: Types of data fragments in archetypes


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.. Creating manual mappings

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SNOMED concepts are required to study semantic proximity.

Problem: Bindings to SNOMED concepts are very scarce.

We manually created mappings through manual searches.

This process was very tedious and time-consuming.


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.. Evaluating the semantic proximity

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We checked if the concepts mapped to these archetype fragmentsare linked through hierarchical and logical relationships of theSNOMED terminology.


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.. Results

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A high ratio of archetype terms are semantically related:30% of Element fragments are subtypes of the Root fragment.80% of Value fragments are connected through hierarchicalrelationships to any other Value fragment of the archetype


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.. Conclusion

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A high ratio of archetype terms are semantically related↓

The network of SNOMED relationships shouldbe exploited during the automated mapping of

archetypes.


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MethodsEvaluationResultsDiscussion

.. Outline


...2 Objectives




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.. Automated mapping between archetypes and SNOMED

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Several mapping techniques were developed tobind archetype fragments to SNOMED concepts:

Lexical techniques

Linguistic resource-based techniques

Terminological context-based techniques

Structure-based techniques


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.. Lexical techniques

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Lexical techniques identify SNOMED concepts andarchetype terms with similar names.

Both the archetype terms and SNOMED descriptions are nor-malized, including plurals, singulars, case-insensitive, etc.

Example:


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.. Linguistic resource-based techniques

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We used several linguistic resources provided by the UMLS.

These resources use a knowledge-intensive approach based onsymbolic and natural-language processing, to discover biomed-ical concepts referred in a text.

Example:


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.. Degree of lexical similarity

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Two degrees of lexical similarity:Full or exact match occurs when an archetype terms is exactlythe same as some SNOMED CT description after normalization.

Partial match occurs when the archetype term is contained in-side some SNOMED CT description.

Example:


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.. Mapping algorithm

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The mapping algorithm consists of three steps:

...1 Exact Match in entire SNOMED...2 Partial Match in SNOMED contexts...3 Structural similarity


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.. First step of Mapping: Exact Match in entire SNOMED

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Lexical and linguistic techniques are used to discover exact oralmost exact correspondeces between all the SNOMED conceptsand archetype fragments


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.. Second step of Mapping: Partial Match in SNOMED contexts

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SNOMED contexts are extracted from the concepts obtained inthe previous step.


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.. Second step of Mapping: Partial Match in SNOMED contexts

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Lexical and linguistic techniques are used to discover partial andapproximate correspondeces in the extracted contexts.


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.. Third step of Mapping: Structural similarity

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Structure-based techniques identify structural similarities betweenthe tree structure of archetypes and the network of SNOMED rela-tionships → Equivalent entities with different names can be mapped


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.. Evaluation

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Our mapping algorithm was applied to 25 archetypes.

The evaluation entailed the automated revision of each mappinggenerated by the method against the manual mappings that wehad created for the study.


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.. Results


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.. Results: Comparison with other approaches


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.. Discussion

.Lessons learned in the work..

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Objetive 1: To understand better how archetype clinical infor-mation is semantically related.

We now know the frequency and type of semanticrelationships between data fragments of archetypes.

We found that a high ratio of archetype fragments are seman-tically related.


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.. Discussion

.Lessons learned in the work..

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Objetive 2: To evaluate whether a combination of context andstructure-based techniques with lexical and linguistic techniquescan improve the automated mapping.

Context and structure-based techniques are able to exploitSNOMED CT relationships to improve the automated mappingof archetypes.


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.. Future Work

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We will test the automatic mapping with different types ofarchetypes.

We will explore ways to use medical knowledge from SNOMED:

To facilitate the semi-automatic creation of new archetypes.

To recommend extensions in existing archetypes.


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.. Summary

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Clinical models, such as archetypes, need to be inte-grated with terminologies to capture, use and transferclinical data in a standard way.

Manual integration is very time-consuming...


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.. Summary

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We propose a novel automated method to link datafragments of archetypes with concepts of medical ter-minologies.

Besides conventional name-based techniques, themethod exploits the medical knowledge representedin SNOMED to improve the automated mapping andthus reduce human participation in the process.


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.. Acknowledgements

Thank you for your attention!

MEC National research project Gestion de Terminologıas Medicaspara Arquetipos TIN2009-14159-C05-05


Health & Medicine

A Study of Semantic Proximity between Archetype Terms based on SNOMED CT Relationships