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Results for “Unified Medical Language System”

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PubMed1999

Representing genomic knowledge in the UMLS semantic network.

Genomics research has a significant impact on the understanding and treatment of human hereditary diseases, and biomedical literature concerning the genome project is becoming more and more important for clinicians. The Unified Medical Language System (UMLS) is designed to facilitate the retrieval and integration of information from multiple-readable biomedical information resources. This paper describes our efforts to integrate concepts important to genomics research with the UMLS semantic network. We found that the UMLS contains over 30 semantic types and most of the semantic relations that are essential for representing the underlying genomic knowledge. In addition, we observed that the organization of the network was appropriate for representing the hierarchical organization of the concepts. Because some of the concepts critical to the genomic domain were found to be missing, we propose to extend the network by adding six new semantic types and sixteen new semantic relations.

Genome, Human↗

PubMed2005

Generating models of surgical procedures using UMLS concepts and multiple sequence alignment.

Surgical procedures can be viewed as a process composed of a sequence of steps performed on, by, or with the patient's anatomy. This sequence is typically the pattern followed by surgeons when generating surgical report narratives for documenting surgical procedures. This paper describes a methodology for semi-automatically deriving a model of conducted surgeries, utilizing a sequence of derived Unified Medical Language System (UMLS) concepts for representing surgical procedures. A multiple sequence alignment was computed from a collection of such sequences and was used for generating the model. These models have the potential of being useful in a variety of informatics applications such as information retrieval and automatic document generation.

Abstracting and Indexing↗

PubMedDate not supplied

Knowledge-based approaches to the maintenance of a large controlled medical terminology.

OBJECTIVE: Develop a knowledge-based representation for a controlled terminology of clinical information to facilitate creation, maintenance, and use of the terminology. DESIGN: The Medical Entities Dictionary (MED) is a semantic network, based on the Unified Medical Language System (UMLS), with a directed acyclic graph to represent multiple hierarchies. Terms from four hospital systems (laboratory, electrocardiography, medical records coding, and pharmacy) were added as nodes in the network. Additional knowledge about terms, added as semantic links, was used to assist in integration, harmonization, and automated classification of disparate terminologies. RESULTS: The MED contains 32,767 terms and is in active clinical use. Automated classification was successfully applied to terms for laboratory specimens, laboratory tests, and medications. One benefit of the approach has been the automated inclusion of medications into multiple pharmacologic and allergenic classes that were not present in the pharmacy system. Another benefit has been the reduction of maintenance efforts by 90%. CONCLUSION: The MED is a hybrid of terminology and knowledge. It provides domain coverage, synonymy, consistency of views, explicit relationships, and multiple classification while preventing redundancy, ambiguity (homonymy) and misclassification.

Computer Simulation↗

PubMed1992

Enhancing the Metathesaurus with clinically relevant concepts: anatomic representations.

To create a comprehensive taxonomy for medical concepts it is necessary to identify gaps and reconcile differences that exist between clinical, bibliographic, and other source vocabularies. As part of the Unified Medical Language System project, we have proposed enhancements to the Metathesaurus by the inclusion of terms from two source vocabularies with different unique perspectives or views. This process has disclosed a number of issues that arise as complexity increases. These issues must be resolved if the resultant Metathesaurus is to support the variety of uses for which it is intended.

Abstracting and Indexing↗

PubMed2000

Knowledge engineering the UMLS.

In order to transform a major portion of the Unified Medical Language System (UMLS) into a formally sound description logics system we have developed a four-step knowledge engineering approach. We report on experiments with a Loom knowledge base which consists of 164,000 concepts and 76,000 relations covering human anatomy and pathology. We discuss the fully automatized as well as interactive semiautomatic steps. The latter ones focus on the manual work necessary to render the formal target representation structures complete and adequate.

Artificial Intelligence↗

PubMed2002

Tracking meaning over time in the UMLS Metathesaurus.

The Unified Medical Language System(R) (UMLS) Metathesaurus contains records arranged by concept or meaning. Each concept contains a unique identifier (CUI) that can be used to track the concept over time. Since the January 2001 release, the Metathesaurus has included the file MRCUI that contains mappings for CUIs that disappear. This paper describes the processes that facilitated this effort and the ongoing effort to find suitable mappings for concepts whose meanings no longer exist in the Metathesaurus. This study highlights the need to identify missed synonymy prior to a release. It also shows a need to work more closely with source providers to identify the closest match in the Metathesaurus when they eliminate terms from their vocabularies.

National Library of Medicine (U.S.)↗

PubMedDate not supplied

Representing the UMLS as an object-oriented database: modeling issues and advantages.

OBJECTIVE: The Unified Medical Language System (UMLS) combines many well-established authoritative medical informatics terminologies in one knowledge representation system. Such a resource is very valuable to the health care community and industry. However, the UMLS is very large and complex and poses serious comprehension problems for users and maintenance personnel. The authors present a representation to support the user's comprehension and navigation of the UMLS. DESIGN: An object-oriented database (OODB) representation is used to represent the two major components of the UMLS-the Metathesaurus and the Semantic Network-as a unified system. The semantic types of the Semantic Network are modeled as semantic type classes. Intersection classes are defined to model concepts of multiple semantic types, which are removed from the semantic type classes. RESULTS: The authors provide examples of how the intersection classes help expose omissions of concepts, highlight errors of semantic type classification, and uncover ambiguities of concepts in the UMLS. The resulting UMLS OODB schema is deeper and more refined than the Semantic Network, since intersection classes are introduced. The Metathesaurus is classified into more mutually exclusive, uniform sets of concepts. The schema improves the user's comprehension and navigation of the Metathesaurus. CONCLUSIONS: The UMLS OODB schema supports the user's comprehension and navigation of the Metathesaurus. It also helps expose and resolve modeling problems in the UMLS.

Databases as Topic↗

PubMed2003

UMLS language and vocabulary tools.

A variety of resources developed for use with the Unified Medical Language System are presented. These resources include the UMLS Knowledge Source Server, the SPECIALIST lexicon, a set of lexical tools that work with the SPECIALIST lexicon, and a variety of other NLP document processing tools. These tools manage lexical variation, tokenize and parse text strings, suggest spelling variants, and provide text-to-concept mapping capabilities. The UMLS Knowledge Source Server is available under a license agreement. The other tools are freely downloadable.

Abstracting and Indexing↗

PubMed1998

Beyond synonymy: exploiting the UMLS semantics in mapping vocabularies.

The Unified Medical Language System (UMLS) contains semantic information about terms from various sources, each concept can be understood and located by its relationships to other concepts: this is a result of the organizing principle of semantic locality. We describe a method in which the semantic relationships between concepts are used to map concepts from different vocabularies in the UMLS. Applied to mapping concepts to MeSH, this method is able to map 50 to 65% of the non-MeSH concepts to MeSH. A manual review of the mapping shows a relevance rate of 61%. Causes of failure include a lack of consistently represented relationships in the UMLS, and some inconsistencies in the categorization of the concepts. The limits of this method are discussed, as well as possible adaptations for other uses.

Algorithms↗

PubMed1993

From ICD9-CM to MeSH using the UMLS: a how-to guide.

One purpose of the Unified Medical Language System (UMLS) is to facilitate conversion of terms from one controlled medical vocabulary to another. We examined our ability to convert International Classification of Diseases, 9th Edition, Clinical Modifications (ICD9-CM) to Medical Subject Headings (MeSH) using the UMLS. We describe a method which mapped 30.4% of ICD9-CM to UMLS. Of these, 95.0% were linked to MeSH, of which translation was straightforward in 90.4%. We discuss the use of these translations for retrieval from MeSH-indexed databases, such as Medline.

Disease↗

PubMed2000

Discovering missed synonymy in a large concept-oriented Metathesaurus.

The Unified Medical Language System (UMLS) [1, 2] Metathesuarus is concept-oriented; its goal is to unite all names with identical meaning in a single Concept. The names come from its constituent vocabularies or "sources"--a wide variety of biomedical terminologies including many controlled vocabularies and classifications used in patient records, administrative health data, bibliographic, research, full-text, and expert systems. Many offer little definitional information, and many are not themselves concept-oriented, so identifying synonymy is a challenging semantic task [3]. The rapidly increasing size of the Metathesaurus makes the task daunting, demanding effective computational support; there are more than 1.5 million names for 730,000 concepts in the January 2000 release. Vocabularies are added and updated using sophisticated lexical matching, selective algorithms, and expert review [4, 5, 6]. Yet the result is imperfect; we have discovered and corrected missed synonymy in approximately 1% of previously released concepts each year. This paper reviews general methods for finding missed synonymy and describes several specific novel approaches which we have found effective.

Algorithms↗

PubMed1992

An evaluation of the source selection elements of the prototype UMLS Information Sources Map.

The Information Sources Map (ISM) is a component of the National Library of Medicine's Unified Medical Language System (UMLS) project. The ISM is intended to provide both human-readable and machine-interpretable information about the content, scope, and access conditions for various information sources such as databases, expert systems, and the organizations which make these information sources available. Automated source selection is supported by three types of indexing in the ISM: Medical Subject Heading (MeSH) terms and subheadings; Semantic Types from the UMLS Semantic Network; and Semantic Type Relations, which depict pairs of semantic types joined by a relationship chosen from the Semantic Network. This paper reports a study of the recall and precision of the source selection elements in the prototype version of the ISM.

Abstracting and Indexing↗

PubMed2003

"Understanding" medical school curriculum content using KnowledgeMap.

OBJECTIVE: To describe the development and evaluation of computational tools to identify concepts within medical curricular documents, using information derived from the National Library of Medicine's Unified Medical Language System (UMLS). The long-term goal of the KnowledgeMap (KM) project is to provide faculty and students with an improved ability to develop, review, and integrate components of the medical school curriculum. DESIGN: The KM concept identifier uses lexical resources partially derived from the UMLS (SPECIALIST lexicon and Metathesaurus), heuristic language processing techniques, and an empirical scoring algorithm. KM differentiates among potentially matching Metathesaurus concepts within a source document. The authors manually identified important "gold standard" biomedical concepts within selected medical school full-content lecture documents and used these documents to compare KM concept recognition with that of a known state-of-the-art "standard"-the National Library of Medicine's MetaMap program. MEASUREMENTS: The number of "gold standard" concepts in each lecture document identified by either KM or MetaMap, and the cause of each failure or relative success in a random subset of documents. RESULTS: For 4,281 "gold standard" concepts, MetaMap matched 78% and KM 82%. Precision for "gold standard" concepts was 85% for MetaMap and 89% for KM. The heuristics of KM accurately matched acronyms, concepts underspecified in the document, and ambiguous matches. The most frequent cause of matching failures was absence of target concepts from the UMLS Metathesaurus. CONCLUSION: The prototypic KM system provided an encouraging rate of concept extraction for representative medical curricular texts. Future versions of KM should be evaluated for their ability to allow administrators, lecturers, and students to navigate through the medical curriculum to locate redundancies, find interrelated information, and identify omissions. In addition, the ability of KM to meet specific, personal information needs should be assessed.

Abstracting and Indexing↗

PubMed2005

Identifying UMLS concepts from ECG Impressions using KnowledgeMap.

Electrocardiogram (ECG) impressions represent a wealth of medical information for potential decision support and drug-effect discovery. Much of this information is inaccessible to automated methods in the free-text portion of the ECG report. We studied the application of the KnowledgeMap concept identifier (KMCI) to map Unified Medical Language System (UMLS) concepts from ECG impressions. ECGs were processed by KMCI and the results scored for accuracy by multiple raters. Reviewers also recorded unidentified concepts through the scoring interface. Overall, KMCI correctly identified 1059 out of 1171 concepts for a recall of 0.90. Precision, indicating the proportion of ECG concepts correctly identified, was 0.94. KMCI was particularly effective at identifying ECG rhythms (330/333), perfusion changes (65/66), and noncardiac medical concepts (11/11). In conclusion, KMCI is an effective method for mapping ECG impressions to UMLS concepts.

Abstracting and Indexing↗

PubMed2006

Creation and implications of a phenome-genome network.

Although gene and protein measurements are increasing in quantity and comprehensiveness, they do not characterize a sample's entire phenotype in an environmental or experimental context. Here we comprehensively consider associations between components of phenotype, genotype and environment to identify genes that may govern phenotype and responses to the environment. Context from the annotations of gene expression data sets in the Gene Expression Omnibus is represented using the Unified Medical Language System, a compendium of biomedical vocabularies with nearly 1-million concepts. After showing how data sets can be clustered by annotative concepts, we find a network of relations between phenotypic, disease, environmental and experimental contexts as well as genes with differential expression associated with these concepts. We identify novel genes related to concepts such as aging. Comprehensively identifying genes related to phenotype and environment is a step toward the Human Phenome Project.

Aging↗

PubMed2006

ADAM: another database of abbreviations in MEDLINE.

MOTIVATION: Abbreviations are an important type of terminology in the biomedical domain. Although several groups have already created databases of biomedical abbreviations, these are either not public, or are not comprehensive, or focus exclusively on acronym-type abbreviations. We have created another abbreviation database, ADAM, which covers commonly used abbreviations and their definitions (or long-forms) within MEDLINE titles and abstracts, including both acronym and non-acronym abbreviations. RESULTS: A model of recognizing abbreviations and their long-forms from titles and abstracts of MEDLINE (2006 baseline) was employed. After grouping morphological variants, 59 405 abbreviation/long-form pairs were identified. ADAM shows high precision (97.4%) and includes most of the frequently used abbreviations contained in the Unified Medical Language System (UMLS) Lexicon and the Stanford Abbreviation Database. Conversely, one-third of abbreviations in ADAM are novel insofar as they are not included in either database. About 19% of the novel abbreviations are non-acronym-type and these cover at least seven different types of short-form/long-form pairs. AVAILABILITY: A free, public query interface to ADAM is available at http://arrowsmith.psych.uic.edu, and the entire database can be downloaded as a text file.

Animals↗

PubMed2003

Modeling and implementing a health information provider on the Internet.

OBJECTIVES: To model and implement a medical web portal providing access to well qualified and high-quality information. MATERIAL AND METHODS: The Unified Medical Language System (UMLS) knowledge sources of the U.S. National Library of Medicine and an Information Sources Catalog (ISC) resulting from the ARIANE project are described. The XML technology that allows files transformations by the means of XSLT is briefly presented. RESULTS: The design and implementation of software modules that exploit knowledge sources, operate the translation of a user's query to selected information sources, and wrap obtained results are detailed. DISCUSSION: Current implementation and wrapping perspectives are discussed in terms of integration and interoperability of health information resources.

Humans↗

PubMed2003

The UMLS Semantic Network and the Semantic Web.

The Unified Medical Language System is an extensive source of biomedical knowledge developed and maintained by the US National Library of Medicine (NLM) and is being currently used in a wide variety of biomedical applications. The Semantic Network, a component of the UMLS is a structured description of core biomedical knowledge consisting of well defined semantic types and relationships between them. We investigate the expressiveness of DAML+OIL, a markup language proposed for ontologies on the Semantic Web, for representing the knowledge contained in the Semantic Network. Requirements specific to the Semantic Network, such as polymorphic relationships and blocking relationship inheritance are discussed and approaches to represent these in DAML+OIL are presented. Finally, conclusions are presented along with a discussion of ongoing and future work.

Programming Languages↗