Alan L Rector

Erik Sundvall

Geraldine Wade

Gergely Heja

Gunnar O Klein

Holger Stenzhorn

ICD-10

Iulian Alecu

James J Cimino

Kent A Spackman

MESH

Nicolette F de Keizer

Patrick Ruch

Roger Côté

Ronald Cornet

SNOMED

Stefan Schulz

UMLS

Yefeng Wang

BMC Medical Informatics and Decision Making

English

PubMed

Springer - Publisher Connector

BioMed Central
BMC Medical Informatics and 
Decision Making
ssOpen AcceIntroduction
SNOMED CT – advances in concept mapping, retrieval, and 
ontological foundations. Selected contributions to the Semantic 
Mining Conference on SNOMED CT (SMCS 2006)
Stefan Schulz*1 and Gunnar O Klein2
Address: 1Institute of Medical Biometry and Medical Informatics, University Medical Center, Freiburg, Germany and 2Department of Microbiology, 
Tumor, and Cell Biology, Karolinska Institutet, Sweden
Email: Stefan Schulz* - stschulz@uni-freiburg.de; Gunnar O Klein - gunnar.klein@ki.se
* Corresponding author    
Medical science and health care both possess a strong tra-
dition of structuring their terminological knowledge
through controlled vocabularies, such as nomenclatures,
thesauri, or classification systems [1-6]. The availability of
such resources mirrors the need for sharing a common
understanding of the employed terms and the semantic
relations holding between them. Community-wide efforts
such as the Unified Medical Language System (UMLS) [7],
have reached a level of both coverage and depth today
that is unmatched within many if not most other scientific
disciplines.
The reason behind investing such a tremendous effort
into those terminological systems is evident: They are
intended to provide support for a wide range of different
tasks, such as unambiguous, focused access to patient data
for retrieval and decision support, the comparison of clin-
ical cases, the retrieval of similar cases, clinical epidemiol-
ogy and research, billing and accounting activities, the
generation of health statistics from relevant and valid
data, semantic interoperability between several (themati-
cally overlapping) data sources, the quality management
of medical services, as well as the structuring of scientific
literature repositories and experimental databases.
So, the question may be raised whether the current bio-
medical terminological resources are already sufficient to
properly serve the expressed needs. Basically, all terminol-
ogy systems in current routine use rest on informal speci-
fications. Their semantics are essentially rooted in a
human understanding of natural language and implicit
assumptions about the taxonomic, partonomic and other
unspecified relations between terms. Interpreting such
relations in light of a particular search, a decision support
problem or, even more challenging, the drawing of ad hoc
inferences often leads to strange and erroneous results.
This is usually due to a lack of any rigid, formal semantics
and ontological foundation underlying the respective ter-
minological systems, an issue that has been increasingly
addressed in recent years.
Whereas current biomedical terminology systems such as
ICD, ICF, LOINC, CMPU, ICNP and many others are
highly focused on quite well-delimited tasks and sub-
domains, the vision of a universal clinical terminology,
covering a broad range of health-related domains and
meeting the needs of all health professionals, has stimu-
lated numerous health informatics research activities.
Is this vision now being materialized by the new health
terminology SNOMED CT? [8]
from First European Conference on SNOMED CT
Copenhagen, Denmark. 1–3 October 2006
Published: 27 October 2008
BMC Medical Informatics and Decision Making 2008, 8(Suppl 1):S1 doi:10.1186/1472-6947-8-S1-S1
<supplement> <title> <p>Selected contributions to the First European Conference on SNOMED CT</p> </title> <editor>Stefan Schulz and Gunnar O Klein</editor> <sponsor> <note>Publication of this supplement was supported by EU Network of Excellence "Semantic Interoperability and Data Mining in Biomedicine"</note> </sponsor> <note>Proceedings</note> <url>http://www.biomedcentral.com/content/pdf/1471-6947-8-S1-info.pdf</url> </supplement>
This article is available from: http://www.biomedcentral.com/1472-6947/8/S1/S1
© 2008 Schulz and Klein; licensee BioMed Central Ltd. 
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), 
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Page 1 of 3
(page number not for citation purposes)
BMC Medical Informatics and Decision Making 2008, 8(Suppl 1):S1 http://www.biomedcentral.com/1472-6947/8/S1/S1During the last two decades, SNOMED (Systemized
Nomenclature of Medicine) has been growing from a
pathology-centered vocabulary to a comprehensive, struc-
tured clinical terminology. Even though SNOMED CT is
still rooted in a strong legacy, it is increasingly subscribing
to principles of logics and ontology. This fact, together
with its impressive number of terms in most areas of med-
icine and health care has lead to a growing international
interest. Nine countries have so far joined the Interna-
tional Health Terminology Standards Development
Organization IHTSDO, a non-profit association founded
in 2007 with the task of the development, quality assur-
ance and distribution of SNOMED CT.
However, there are still only very few prototypical imple-
mentations of SNOMED CT in clinical settings, the feasi-
bility of such a comprehensive terminology as basis for
the whole health delivery process is still subject to discus-
sion, and several shortcomings, regarding both SNOMED
CT architeture and content, still persist [9].
The papers in this supplement of BMC Medical Informatics
and Decision Making are extended and updated contribu-
tions to the Semantic Mining Conference on SNOMED CT
(SMCS 2006), organized by the European Union Network
of Excellence "Semantic Interoperability and Data Mining
in Biomedicine" in October 2006.
It was the first European forum on SNOMED CT for
health policy makers, clinicians, nurses, system develop-
ers, computer scientists, terminologists and translators. A
number of prominent invited speakers provided over-
views of the current efforts and developments in the con-
text of SNOMED CT and many scientific contributions
illuminated ongoing research on SNOMED CT. Out of the
22 scientific papers and posters published in the proceed-
ings of this conference, eight were selected for this special
issue by the program committee due to their scientific
excellence. This selection mirrors the different research
strands on SNOMED CT and represents a broad range of
countries, viz., The Netherlands, USA, France, Australia,
Switzerland, Sweden, UK, Hungary, and Germany. We
want to thank the reviewers for their in-depth work in
reviewing first the conference submissions and then again
the selected contributions. The following contributions
are included in this special issue:
In their article Forty years of SNOMED: a literature review
[10] Ronald Cornet and Nicolette de Keizer provide an
overview of published studies on SNOMED over a period
of 40 years, reflected in scientific publications. They found
that most studies concern SNOMED in theory and a
minor number provides an account of the use of
SNOMED in practice. (This is also a clear tendency regard-
ing the papers in this special issue).
A major challenge of the adoption of a world-wide termi-
nology is the use of legacy terminologies tailored and
optimized to meet specific coding and documentation
requirements. Therefore, a seamless migration from pro-
prietary solutions to a common standard requires high-
quality rules for manual cross-terminology mapping as
described by Geraldine Wade and Trent Rosenbloom in
their paper Experiences Mapping a Legacy Interface Terminol-
ogy to SNOMED CT [11] which emphasizes the value of
discoveries resulting from this mapping as important con-
tributions to the refinement of SNOMED CT.
The relation between SNOMED CT and a legacy terminol-
ogy is also addressed by Iulian Alecu, Cedric Bousquet,
and Marie-Christine Jaulent in their paper A case report:
Using SNOMED CT for grouping Adverse Drug Reactions
Terms [12]. The authors provide evidence that the logical
structure of SNOMED CT can be employed to improve
term grouping and retrieval in the WHO Adverse Reaction
Terminology, important for clinical trials and medical
care.
Another mapping experience is reported by Yefeng Wang,
Jon Patrick, Graeme Miller, and Julie O'Hallaran in A
Computational Linguistics Motivated Mapping of ICPC-2
PLUS to SNOMED CT [13]. The authors compare different
terminology mapping approaches including language
engineering methods and also address the problem aris-
ing from the fact that one source concept maps to the
coordination of two target concepts.
Computational linguistics approaches are also employed
by Patrick Ruch, Julien Gobeill, Christian Lovis, and Anto-
ine Geissbühler. [14] In their contribution Automatic Med-
ical Encoding with SNOMED Categories they present two
information retrieval approaches that address both the
retrieval of SNOMED CT concepts and the automated
encoding of free text and report on a first evaluation of a
prototype.
The encoding of clinical data using information models
and archetypes is addressed by Erik Sundvall, Rahil
Qamar, Mikael Nyström, Mattias Forss, Håkan Petersson,
Hans Åhlfeldt, and Alan Rector. In their paper Integration
of Tools for Binding Archetypes to SNOMED CT [15] they
present an approach that supports this task. They also dis-
cuss the yet unresolved problems of binding clinical infor-
mation models to SNOMED CT and the control of post-
coordination of concepts.
Two articles deal with the ontological background of
SNOMED CT, an issue that has repeatedly been advocated
in recent years. [9,16-18]Page 2 of 3
(page number not for citation purposes)
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In the first one, entitled Ontological analysis of SNOMED
CT [19], Gergely Héja, György Surján, and Péter Varga per-
form an analysis of the structure of SNOMED CT based on
the formal top-level ontology DOLCE. They present a
typology of errors occuring when the SNOMED CT hierar-
chies are submitted to formal ontological scrutiny and
provide suggestions of how to avoid these errors.
In the second one, Formal Representation of Complex
SNOMED CT Expressions [20], Stefan Schulz, Kornél
Markó, and Boontawee Suntisrivaraporn focus on the rep-
resentation of complex events and procedures, highlight
the limited expressiveness of SNOMED CT regarding
negations and formally describe the ambiguities in the
representation of complex concepts.
Due to the recently facilitated access to the SNOMED CT
sources, research activities are increasing all over the
world and the interchange between the SNOMED CT
community and the academic world is strengthening. Evi-
dence for this is given by the recent MEDINFO, MIE and
AMIA conferences as well as by the recent AMIA KR-MED
2008 conference on "Representing and Sharing Knowl-
edge using SNOMED CT".
We hope that you will enjoy this special issue and that it
helps you keeping track of some of the fascinating
research in applied terminology, ontology and clinical ter-
minologies.
Competing interests
The authors declare that they have no competing interests.
Acknowledgements
The publishing of this supplement was supported by the EU Network of 
Excellence Semantic Interoperability and Data Mining in Biomedicine (NoE 
507505).
This article has been published as part of BMC Medical Informatics and Deci-
sion Making Volume 8 Supplement 1, 2008: Selected contributions to the 
First European Conference on SNOMED CT. The full contents of the sup-
plement are available online at http://www.biomedcentral.com/1472-6947/
8?issue=S1.
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SNOMED CT – advances in concept mapping, retrieval, and ontological foundations. Selected contributions to the Semantic Mining Conference on SNOMED CT (SMCS 2006)

Crossref

Schulz, Stefan

Klein, Gunnar O.

FreiDok plus

Schulz, S

Klein, GO

Swepub

SNOMED CT - advances in concept mapping, retrieval, and ontological foundations. Selected contributions to the Semantic Mining Conference on SNOMED CT (SMCS 2006)

A computational linguistics motivated mapping of

Adapting clinical ontologies in real-world environments.

de Keizer Nicolette: Forty years of SNOMED: a literature review.

Dhaen Christoffel: Ontology-based error detection in SNOMED CT.

Examining SNOMED from the perspective of formal ontological principles: Some preliminary analysis and observations. KR-MED

Geissbühler Antoine: Automatic medical encoding with SNOMED categories.

ICD-10: International Statistical Classification of Diseases and Health Related Problems. 10th Revision Geneva: World Health Organization;

Issue 129 Edited by: Klaus A

Jaulent Marie-Christine: A case report: Using SNOMED CT for grouping adverse drug reactions terms. BMC Medical Informatics and Medical Decision Making 2008, 8(Suppl 1):S4.

L: Clinical terminology: Why is it so hard?

Louise: The Systemised Nomenclature of Medicine:

Markó Kornél, Suntisrivaraporn Boontawee: Formal representation of complex SNOMED ct expressions. BMC Medical Informatics and Medical Decision Making 2008, 8(Suppl 1):S9.

Qamar Rahil, Nyström Mikael, Forss Mattias, Petersson Håkan, Åhlfeldt Hans, Rector Alan: Integration of tools for binding archetypes to SNOMED CT. BMC Medical Informatics and Medical Decision Making 2008, 8(Suppl 1):S7.

Rosenbloom Trent: Experiences mapping a legacy interface terminology to SNOMED CT. BMC Medical Informatics and Medical Decision Making 2008, 8(Suppl 1):S3.

Suntisrivaraporn Boontawee, Baader Franz: SNOMED CT's problem list: Ontologists' and logicians' therapy suggestions.

Surján György, Varga Péter: Ontological analysis of SNOMED CT.

Terms

Xinxin: The practical impact of ontologieson biomedical informatics.

Zwetsloot-Schonk JH: Understanding terminological systems. i: Terminology and typology.

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SNOMED CT – advances in concept mapping, retrieval, and ontological foundations. Selected contributions to the Semantic Mining Conference on SNOMED CT (SMCS 2006)

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