The Unified Medical Language System (UMLS) is a
compendium of many
controlled vocabularies in the
biomedical sciences (created 1986[1]).
It provides a mapping structure among these vocabularies and thus
allows one to translate among the various terminology systems; it
may also be viewed as a comprehensive
thesaurus and
ontology of biomedical concepts. UMLS further provides
facilities for
natural language processing. It is intended to be used mainly by
developers of systems in
medical informatics.
UMLS consists of Knowledge Sources (databases) and a set of software
tools.
The UMLS was designed and is maintained by the
US
National Library of Medicine, is updated quarterly and may be used
for free. The project was initiated in 1986 by
Donald A. B. Lindberg,
M.D., then and current Director of the Library of Medicine.
Purpose and
applications
The number of biomedical resources available to researchers is
enormous. Often this is a problem due to the large volume of documents
retrieved when the medical literature is searched. The purpose of the
UMLS is to enhance access to this literature by facilitating the
development of computer systems that understand biomedical language.
This is achieved by overcoming two significant barriers: "the variety of
ways the same concepts are expressed in different machine-readable
sources & by different people" and "the distribution of useful
information among many disparate databases & systems".
Licensing
Users of the system are required to sign a "UMLS agreement" and file
brief annual usage reports. Academic users may use the UMLS free of
charge for research purposes. Commercial or production use requires
copyright licenses for some of the incorporated source vocabularies.
Knowledge Sources
Metathesaurus
The Metathesaurus forms the base of the UMLS and comprises over 1
million biomedical concepts and 5 million concept names, all of which
stem from the over 100 incorporated controlled vocabularies and
classification systems. Some examples of the incorporated controlled
vocabularies are
ICD-10,
MeSH,
SNOMED CT,
DSM-IV,
LOINC,
WHO Adverse Drug Reaction Terminology,
UK Clinical Terms,
RxNorm,
Gene Ontology, and
OMIM (see
full list).
The Metathesaurus is organized by concept, and each concept has
specific attributes defining its meaning and is linked to the
corresponding concept names in the various source vocabularies. Numerous
relationships between the concepts are represented, for instance
hierarchical ones such as "isa"
for subclasses and "is part of" for subunits, and associative ones such
as "is caused by" or "in the literature often occurs close to" (the
latter being derived from
Medline).
The scope of the Metathesaurus is determined by the scope of the
source vocabularies. If different vocabularies use different names for
the same concept, or if they use the same name for different concepts,
then this will be faithfully represented in the Metathesaurus. All
hierarchical information from the source vocabularies is retained in the
Metathesaurus. Metathesaurus concepts can also link to resources outside
of the database, for instance gene sequence databases.
Semantic Network
Each concept in the Metathesaurus is assigned one or more semantic
types (categories), which are linked with one another through
semantic relationships.[2]
The
semantic network is a catalog of these semantic types and
relationships. This is a rather broad classification; there are 135
semantic types and 54 relationships in total.
The major semantic types are organisms, anatomical structures,
biologic function, chemicals, events, physical objects, and concepts or
ideas. The links among semantic types define the structure of the
network and show important relationships between the
groupings and concepts. The primary link between semantic types is
the "isa"
link, establishing a
hierarchy of types. The network also has 5 major categories of
non-hierarchical (or associative) relationships, which constitute the
remaining 53 relationship types. These are "physically related to",
"spatially related to", "temporally related to", "functionally related
to" and "conceptually related to".[2]
The information about a semantic type includes an identifier,
definition, examples, hierarchical information about the encompassing
semantic type(s), and
associative relationships. Associative relationships within the
Semantic Network are very weak. They capture at most some-some
relationships, i.e. they capture the fact that some instance of the
first type may be connected by the salient relationship to some instance
of the second type. Phrased differently, they capture the fact that a
corresponding relational assertion is meaningful (though it need not be
true in all cases).
An example of an associative relationship is "may-cause",
applied to the terms (smoking, lung cancer) would yield: smoking "may-cause"
lung cancer.
SPECIALIST Lexicon
The SPECIALIST Lexicon contains information about common English
vocabulary, biomedical terms, terms found in
MEDLINE
and terms found in the UMLS Metathesaurus. Each entry contains
syntactic (how words are put together to create meaning),
morphological (form and structure) and
orthographic (spelling) information. A set of
Java programs use the lexicon to work through the variations in
biomedical texts by relating words by their parts of speech, which can
be helpful in
web searches or searches through an
electronic medical record.
Entries may be one-word or multiple-word terms. Records contain four
parts: base form (i.e. "run" for "running"); parts of speech (of which
Specialist recognizes eleven); a unique identifier; and any available
spelling variants. For example, a
query for "anesthetic" would return the following:
{ base=anaesthetic spelling_variant=anesthetic entry=E0008769 cat=noun variants=reg}{ base=anaesthetic spelling_variant=anesthetic entry=E0008770 cat=adj variants=inv position=attrib(3)}
(Browne et al., 2000)
[3]
The SPECIALIST lexicon is available in two formats. The "unit record"
format can be seen above, and comprises slots and fillers.
A slot is the element (i.e. "base=" or "spelling variant=") and
the fillers are the values attributable to that slot for that
entry. The "relational
table" format is not yet
normalized and contain a great deal of redundant data in the files.
Inconsistencies and other errors
Given the size and complexity of the UMLS and its permissive policy
on integrating terms, errors are inevitable.[4]
Errors include ambiguity and redundancy, hierarchical relationship
cycles (a concept is both an ancestor and descendant to another),
missing ancestors (semantic types of parent and child concepts are
unrelated), and semantic inversion (the child/parent relationship with
the semantic types is not consistent with the concepts).[5]
These errors are discovered and resolved by auditing the UMLS. Manual
audits can be very time-consuming and costly. Researchers have attempted
to address the issue through a number of ways. Automated tools can be
used to search for these errors. For structural inconsistencies (such as
loops), a trivial solution that removes based on order would work.
However, the same wouldn't apply when the inconsistency is at the term
or concept level (context-specific meaning of a term).[6]
This requires an informed search strategy be used (knowledge
representation).
Supporting
software tools
In addition to the knowledge sources, the
National Library of Medicine also provides supporting tools.
- MetamorphoSys - customizes the
Metathesaurus for specific applications, for instance by
excluding certain source vocabularies.
- lvg - a program that uses the SPECIALIST
lexicon to generate lexical variants of a given term and to
support the parsing of natural language text.
- MetaMap - online tool that, when given
an arbitrary piece of text, finds and returns the relevant
Metathesaurus concepts.
- MetaMap Transfer (MMTx) - Java
implementation of MetaMap (no longer supported).
- Knowledge Source Server - web-based
access to vocabularies (being retired Fall 2010
[1]).
Third party
software
See also
References
-
^
Unified Medical Language System, 1996
- ^
a
b
National Library of Medicine (2009).
"Chapter 5 - Semantic Networks". UMLS Reference Manual.
Bethesda, MD: U.S. National Library of Medicine, National Institutes
of Health.
-
^ Browne, McCray and
Srinivasan (2000).
The Specialist Lexicon. Lister Hill National Center for
Biomedical Communications, National Library of Medicine, Bethesda,
MD, p. 1.
-
^
Morrey, CP; Geller, J; Halper, M;
Perl, Y (2009).
"The Neighborhood Auditing Tool: A hybrid interface for auditing the
UMLS". Journal of Biomedical Informatics 42 (3):
468–489.
doi:10.1016/j.jbi.2009.01.006.
PMC 2891659.
PMID 19475725.
-
^
Geller, J; Morrey, CP; Xu, J; Halper,
M; Elhanan, G; Perl, Y; Hripcsack, G (2009).
"Comparing Inconsistent Relationship Configurations Indicating UMLS
Errors". AMIA Annu Symp Proc 2009: 193–197.
PMC 2815406.
PMID 20351848.
-
^
Zhu, Xinxin; Fan, Jung-Wei; Baorto,
David M.; Weng, Chunhua; Cimino, James J. (2009). "A review of
auditing methods applied to the content of controlled biomedical
terminologies". Journal of Biomedical Informatics 42
(3): 413–425.
doi:10.1016/j.jbi.2009.03.003.
PMID 19285571.
Further reading
- Bodenreider, Olivier. (2004)
The Unified Medical Language System (UMLS): integrating biomedical
terminology. Nucleic Acids Research, 32, D267-D270.
- Kumar, Anand and Smith, Barry (2003)
The Unified Medical Language System and the Gene Ontology: Some
Critical Reflections, in: KI 2003: Advances in Artificial
Intelligence (Lecture Notes in Artificial Intelligence 2821),
Berlin: Springer, 135–148.
- Smith, Barry Kumar, Anand and Schulze-Kremer, Steffen (2004)
Revising the UMLS Semantic Network, in M. Fieschi, et al.
(eds.), Medinfo 2004, Amsterdam: IOS Press, 1700.
- Coiera, Enrico (2003). Guide to
Health Informatics (2nd ed.). Modder, Arnold.
ISBN 0-340-76425-2.
- Mougin, Fleur; Bodenreider,
Oliver (2005).
"Approaches to Eliminating Cycles in the UMLS Metathesaurus: Naïve
vs. Formal". AMIA Annual Symposium Proceedings: 550–554.
PMC 1560864.
PMID 16779100.
External links