SEARCH · PubMed Health

Results for “Unified Medical Language System”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5Linked to original sources

PubMed1998

Extracting medical knowledge for a coded problem list vocabulary from the UMLS Knowledge Sources.

INTRODUCTION: The Unified Medical Language System (UMLS) Knowledge Sources embody a rich source of medical knowledge. We sought to extract a portion of this knowledge by incorporating information about relationships between UMLS concepts into an existing problem list vocabulary. METHODS: We matched terms from the coded problem list of The Medical Record (TMR), a computer-based patient record system, with those found in the UMLS Metathesaurus. Those UMLS concepts that participate in 'parent' relationships with the matched TMR concepts were translated back into TMR codes and the relationship information was retained for integration into the coded problem list of TMR. RESULTS: Of the coded problems currently in use in TMR, 67% (1627/2436) could be matched by normalized string matches to the UMLS Knowledge Sources. Of these matched TMR concepts, 91% (1488/1627) participated in at least one UMLS-identified parent relationship but only 28% of the matched concepts (454/1627) participated in parent relationships that already matched to a TMR code. As a result, although 67% of TMR codes were matched to UMLS concepts, only 19% of our original problem list (454/2436) could be augmented by relationship information contained in UMLS without improving the rate of matches or adding additional UMLS concepts as coded problems in TMR. CONCLUSION: This study illustrates the rapid decline in overall rates of matching that result from a multiplicative effect of successive matches of terms to concepts, concepts to relationships and concepts back to entry terms. This effect will hamper any effort to extract relationship knowledge from the UMLS for incorporation into an entry vocabulary that is not already one of the source vocabularies of the UMLS Metathesaurus.

Humans↗

PubMed1999

Automated semantic indexing of imaging reports to support retrieval of medical images in the multimedia electronic medical record.

This paper describes preliminary work evaluating automated semantic indexing of radiology imaging reports to represent images stored in the Image Engine multimedia medical record system at the University of Pittsburgh Medical Center. The authors used the SAPHIRE indexing system to automatically identify important biomedical concepts within radiology reports and represent these concepts with terms from the 1998 edition of the U.S. National Library of Medicine's Unified Medical Language System (UMLS) Metathesaurus. This automated UMLS indexing was then compared with manual UMLS indexing of the same reports. Human indexing identified appropriate UMLS Metathesaurus descriptors for 81% of the important biomedical concepts contained in the report set. SAPHIRE automatically identified UMLS Metathesaurus descriptors for 64% of the important biomedical concepts contained in the report set. The overall conclusions of this pilot study were that the UMLS metathesaurus provided adequate coverage of the majority of the important concepts contained within the radiology report test set and that SAPHIRE could automatically identify and translate almost two thirds of these concepts into appropriate UMLS descriptors. Further work is required to improve both the recall and precision of this automated concept extraction process.

Abstracting and Indexing↗

PubMed2001

Standardized terminological services enabling semantic interoperability between distributed and heterogeneous systems.

The interconnection of heterogeneous computer applications in medicine raises the issue of semantic interoperability, going beyond traditional approaches of terminological standardization in basically three aspects. First, the variety of medical vocabularies that currently coexist in different domains is a major barrier for the integration of autonomously developed applications. Fortunately, with the Unified Medical Language System (UMLS) there are machine-readable terminological sources that cover and integrate most of the existing medical vocabularies. Second, the exchanged data need to be processed by a machine for different purposes like patient data integration, access to literature and knowledge bases as well as clinical audit and research. Medical vocabularies provided as passive dictionaries are no longer sufficient. Software system developers should take advantage of terminological services for refining user queries, for mapping the user's terms to appropriate medical vocabularies etc. Third, the services should be accessible uniformly and transparently. In the CORBAmed initiative a proposal for a standardized interface for querying and accessing computerized medical terminology resources was created. Based on the mentioned principles the MUSTANG system (Medical UMLS based Terminology Server for Authoring, Navigating and Guiding the Retrieval to Heterogeneous Knowledge Sources) has been developed. It is implemented on a Windows NT platform using the ORACLE database management and development software. The terminological services are accessible via multiple interfaces. The MUSTANG-System and the experiences with using terminological services in practice are described. Opposed to other levels of standardization like syntactical message standards there is much more a hesitation in the use of standardized terminology.

Humans↗

PubMedDate not supplied

Using nurses' natural language entries to build a concept-oriented terminology for patients' chief complaints in the emergency department.

Information about the chief complaint (CC), also known as the patient's reason for seeking emergency care, is critical for patient prioritization for treatment and determination of patient flow through the emergency department (ED). Triage nurses document the CC at the start of the ED visit, and the data are increasingly available in electronic form. Despite the clinical and operational significance of the CC to the ED, there is no standard CC terminology. We propose the construction of concept-oriented nursing terminologies from the actual language used by experts. We use text analysis to extract CC concepts from triage nurses' natural language entries. Our methodology for building the nursing terminology utilizes natural language processing techniques and the Unified Medical Language System.

Computational Biology↗

PubMed1998

An evaluation of UMLS as a controlled terminology for the Problem List Toolkit.

We are developing a set of software components--the Problem List Toolkit (PL-Tk)--to support operations on clinical problem labels. An adaptation of the National Library of Medicine's Unified Medical Language System (UMLS) provides general vocabulary services to domain-specific software components. Our initial investigation centers on the inclusion in UMLS of problem labels used in the Beth Israel Deaconess Medical Center's Online Medical Record (OMR). We also explore the semantic typing of problem labels matched in UMLS. We have operationally defined a clinical problem to derive its semantic type from classes of terms representing findings or processes typically requiring diagnostic evaluation or therapeutic management in clinical practice. Of 1262 unique OMR problem labels, 999 terms (79%) have matches in UMLS. 986 of 999 terms (99%) map to the UMLS concept of the corresponding lexical match. 952 of 999 terms (95%) have semantic types that comply with our operational definition of clinical problems. These 952 terms (75%) constitute Version 1.0 of the problem list vocabulary B196. Matching terms with inappropriate semantic types raise issues regarding requirements for PL-Tk, typing of existing UMLS terms, and the adequacy of our operational definition for clinical problems. UMLS provides a large repertoire of pre-coordinated terms that are used as problem labels in a heavily used computer-based patient record system. The semantic type hierarchy provides a framework for the consistent use of clinical concepts in problem lists such that clinical problem labels represent "good" clinical problems.

Evaluation Studies as Topic↗

PubMed2002

Evaluation and application of a semantic network partition.

Semantic networks (SNs) are excellent knowledge representation structures. However, large semantic networks are hard to comprehend. To overcome this difficulty, several methods of partitioning have been developed that rely on different mixes of structural and semantic methods. However, little has appeared in the literature concerning the question whether a partition of a semantic network creates subnetworks that agree with human insight. We address this issue by presenting a comparison between the results of an algorithmic partitioning method and a partition created by a group of experts. Subsequently, we show how a network partition can be used to generate various partial views of a semantic network, which facilitate user orientation. Examples from the Unified Medical Language System (UMLS) SN are used to demonstrate partial views.

Algorithms↗

PubMedDate not supplied

Planned NLM/AHCPR large-scale vocabulary test: using UMLS technology to determine the extent to which controlled vocabularies cover terminology needed for health care and public health.

The National Library of Medicine (NLM) and the Agency for Health Care Policy and Research (AHCPR) are sponsoring a test to determine the extent to which a combination of existing health-related terminologies covers vocabulary needed in health information systems. The test vocabularies are the 30 that are fully or partially represented in the 1996 edition of the Unified Medical Language System (UMLS) Metathesaurus, plus three planned additions: the portions of SNOMED International not in the 1996 Metathesaurus Read Clinical Classification, and the Logical Observations Identifiers, Names, and Codes (LOINC) system. These vocabularies are available to testers through a special interface to the Internet-based UMLS Knowledge Source Server. The test will determine the ability of the test vocabularies to serve as a source of controlled vocabulary for health data systems and applications. It should provide the basis for realistic resource estimates for developing and maintaining a comprehensive "standard" health vocabulary that is based on existing terminologies.

Computer Communication Networks↗

PubMedDate not supplied

Design and implementation of a national clinical trials registry.

The authors have developed a Web-based system that provides summary information about clinical trials being conducted throughout the United States. The first version of the system, publicly available in February 2000, contains more than 4,000 records representing primarily trials sponsored by the National Institutes of Health. The impetus for this system has come from the Food and Drug Administration (FDA) Modernization Act of 1997, which mandated a registry of both federally and privately funded clinical trials "of experimental treatments for serious or life-threatening diseases or conditions." The system design and implementation have been guided by several principles. First, all stages of system development were guided by the needs of the primary intended audience, patients and other members of the public. Second, broad agreement on a common set of data elements was obtained. Third, the system was designed in a modular and extensible way, and search methods that take extensive advantage of the National Library of Medicine's Unified Medical Language System (UMLS) were developed. Finally, since this will be a long-term effort involving many individuals and organizations, the project is being implemented in several phases.

Clinical Trials as Topic↗

PubMed1994

MD Concept: a model for integrating medical knowledge.

Many integrated clinical information systems depend on large knowledge bases containing dictionary of terms as well as specific information about each term and the relationships between terms. We propose a knowledge base model called MD Concept which is based on a semantic network and uses an object-oriented paradigm and relational tables. A prototype has been developed which integrates the Unified Medical Language System (UMLS) with other databases including the Systematized Nomenclature of Medicine (SNOMED II), the Diagnostic and Statistical Manual of Mental Disorders (DSM-IIIR) and a pharmaceutical database. We demonstrate how a user can easily navigate in this knowledge world using a browser.

Artificial Intelligence↗

PubMed2003

Classifying the precancers: a metadata approach.

BACKGROUND: During carcinogenesis, precancers are the morphologically identifiable lesions that precede invasive cancers. In theory, the successful treatment of precancers would result in the eradication of most human cancers. Despite the importance of these lesions, there has been no effort to list and classify all of the precancers. The purpose of this study is to describe the first comprehensive taxonomy and classification of the precancers. As a novel approach to disease classification, terms and classes were annotated with metadata (data that describes the data) so that the classification could be used to link precancer terms to data elements in other biological databases. METHODS: Terms in the UMLS (Unified Medical Language System) related to precancers were extracted. Extracted terms were reviewed and additional terms added. Each precancer was assigned one of six general classes. The entire classification was assembled as an XML (eXtensible Mark-up Language) file. A Perl script converted the XML file into a browser-viewable HTML (HyperText Mark-up Language) file. RESULTS: The classification contained 4700 precancer terms, 568 distinct precancer concepts and six precancer classes: 1) Acquired microscopic precancers; 2) acquired large lesions with microscopic atypia; 3) Precursor lesions occurring with inherited hyperplastic syndromes that progress to cancer; 4) Acquired diffuse hyperplasias and diffuse metaplasias; 5) Currently unclassified entities; and 6) Superclass and modifiers. CONCLUSION: This work represents the first attempt to create a comprehensive listing of the precancers, the first attempt to classify precancers by their biological properties and the first attempt to create a pathologic classification of precancers using standard metadata (XML). The classification is placed in the public domain, and comment is invited by the authors, who are prepared to curate and modify the classification.

Decision Support Systems, Clinical↗

PubMed2003

The KnowledgeMap project: development of a concept-based medical school curriculum database.

We developed the KnowledgeMap (KM) system as an online, concept-based database of medical school curriculum documents. It uses the KM concept indexer to map full-text documents and match search queries to concepts in the Unified Medical Language System (UMLS). In this paper, we describe the design of KM and report the first seven months of its implementation into a medical school. Despite being emphasized in only two first year courses and one fourth year course, students from all four classes used KM to search and browse documents. All faculty members involved with courses piloting KM used the system to upload and manage lecture documents. Currently, we are working with eight course directors to transition their courses to KM for next year.

Abstracting and Indexing↗

PubMed2005

Indexing UMLS Semantic Types for Medical Question-Answering.

Open-domain Question-Answering (QA) systems heavily rely on named entities, a set of general-purpose semantic types which generally cover names of persons, organizations and locations, dates and amounts, etc. If we are to build medical QA systems, a set of medically relevant named entities must be used. In this paper, we explore the use of the UMLS (Unified Medical Language System) Semantic Network semantic types for this purpose. We present an experiment where the French part of the UMLS Metathesaurus, together with the associated semantic types, is used as a resource for a medically-specific named entity tagger. We also explore the detection of Semantic Network relations for answering specific types of medical questions. We present results and evaluations on a corpus of French-language medical documents that was used in the EQueR Question-Answering evaluation forum. We show, using statistical studies, that strategies for using these new tags in a QA context are to take in account the individual origin of documents.

Humans↗

PubMed2003

Concept-match medical data scrubbing. How pathology text can be used in research.

CONTEXT: In the normal course of activity, pathologists create and archive immense data sets of scientifically valuable information. Researchers need pathology-based data sets, annotated with clinical information and linked to archived tissues, to discover and validate new diagnostic tests and therapies. Pathology records can be used for research purposes (without obtaining informed patient consent for each use of each record), provided the data are rendered harmless. Large data sets can be made harmless through 3 computational steps: (1) deidentification, the removal or modification of data fields that can be used to identify a patient (name, social security number, etc); (2) rendering the data ambiguous, ensuring that every data record in a public data set has a nonunique set of characterizing data; and (3) data scrubbing, the removal or transformation of words in free text that can be used to identify persons or that contain information that is incriminating or otherwise private. This article addresses the problem of data scrubbing. OBJECTIVE: To design and implement a general algorithm that scrubs pathology free text, removing all identifying or private information. METHODS: The Concept-Match algorithm steps through confidential text. When a medical term matching a standard nomenclature term is encountered, the term is replaced by a nomenclature code and a synonym for the original term. When a high-frequency "stop" word, such as a, an, the, or for, is encountered, it is left in place. When any other word is encountered, it is blocked and replaced by asterisks. This produces a scrubbed text. An open-source implementation of the algorithm is freely available. RESULTS: The Concept-Match scrub method transformed pathology free text into scrubbed output that preserved the sense of the original sentences, while it blocked terms that did not match terms found in the Unified Medical Language System (UMLS). The scrubbed product is safe, in the restricted sense that the output retains only standard medical terms. The software implementation scrubbed more than half a million surgical pathology report phrases in less than an hour. CONCLUSIONS: Computerized scrubbing can render the textual portion of a pathology report harmless for research purposes. Scrubbing and deidentification methods allow pathologists to create and use large pathology databases to conduct medical research.

Computing Methodologies↗

PubMedDate not supplied

Searching for biomedical information on the World Wide Web.

The rapid growth of biomedical information available via the Internet and its most popular retrieval system, the World Wide Web, has fostered active research and development directed toward locating resources that are appropriate for answering specific queries. The goal is to create tools that optimize information retrieval (as measured by two quantities, precision and recall) while minimizing the effort required by the user. Existing Web retrieval tools can be divided into the following groups: manually maintained topical lists; automatically generated word-based indices; software agents and multi-index searching aids; network cataloging methods; and miscellaneous hybrid and newer approaches. Improvements in current methods should arise from further research into: methods of describing objects on the Web; improved ways of searching for (and within) collections of documents as opposed to single documents; the ability to search for fielded documents; and ways to describe resources that span intra- and interdisciplinary as well as cross-cultural linguistic differences. For this last problem, the U.S. National Library of Medicine's Unified Medical Language System (UMLS) will be of great help. As online information retrieval improves, efforts are underway to improve the online information itself; quality control over content is being addressed as the peer-review systems of traditional printed journals migrate into the realm of electronic publication.

Abstracting and Indexing↗

PubMedDate not supplied

Phase II evaluation of clinical coding schemes: completeness, taxonomy, mapping, definitions, and clarity. CPRI Work Group on Codes and Structures.

OBJECTIVE: To compare three potential sources of controlled clinical terminology (READ codes version 3.1, SNOMED International, and Unified Medical Language System (UMLS) version 1.6) relative to attributes of completeness, clinical taxonomy, administrative mapping, term definitions and clarity (duplicate coding rate). METHODS: The authors assembled 1929 source concept records from a variety of clinical information taken from four medical centers across the United States. The source data included medical as well as ample nursing terminology. The source records were coded in each scheme by an investigator and checked by the coding scheme owner. The codings were then scored by an independent panel of clinicians for acceptability. Codes were checked for definitions provided with the scheme. Codes for a random sample of source records were analyzed by an investigator for "parent" and "child" codes within the scheme. Parent and child pairs were scored by an independent panel of medical informatics specialists for clinical acceptability. Administrative and billing code mapping from the published scheme were reviewed for all coded records and analyzed by independent reviewers for accuracy. The investigator for each scheme exhaustively searched a sample of coded records for duplications. RESULTS: SNOMED was judged to be significantly more complete in coding the source material than the other schemes (SNOMED* 70%; READ 57%; UMLS 50%; *p < .00001). SNOMED also had a richer clinical taxonomy judged by the number of acceptable first-degree relatives per coded concept (SNOMED* 4.56, UMLS 3.17; READ 2.14, *p < .005). Only the UMLS provided any definitions; these were found for 49% of records which had a coding assignment. READ and UMLS had better administrative mappings (composite score: READ* 40.6%; UMLS* 36.1%; SNOMED 20.7%, *p < .00001), and SNOMED had substantially more duplications of coding assignments (duplication rate: READ 0%; UMLS 4.2%; SNOMED* 13.9%, *p < .004) associated with a loss of clarity. CONCLUSION: No major terminology source can lay claim to being the ideal resource for a computer-based patient record. However, based upon this analysis of releases for April 1995, SNOMED International is considerably more complete, has a compositional nature and a richer taxonomy. Is suffers from less clarity, resulting from a lack of syntax and evolutionary changes in its coding scheme. READ has greater clarity and better mapping to administrative schemes (ICD-10 and OPCS-4), is rapidly changing and is less complete. UMLS is a rich lexical resource, with mappings to many source vocabularies. It provides definitions for many of its terms. However, due to the varying granularities and purposes of its source schemes, it has limitations for representation of clinical concepts within a computer-based patient record.

Abstracting and Indexing↗

PubMed1995

NeuroNames Brain Hierarchy.

The NeuroNames Brain Hierarchy is a structured system of neuroanatomical terminology that provides a comprehensive representation of virtually all human and nonhuman primate brain structures that are identifiable either grossly or in Niss1-stained histological sections. This system was devised for computer applications to address deficiencies in the brain terminology presented in Nomina Anatomica. English terms are listed for 783 structures in nine levels of hierarchical ranking. Abbreviations are provided for all superficial and primary volumetric structures. The substructures that constitute the total volume of every superstructure are identified. Superficial features of the brain are clearly distinguished from internal, volumetric brain structures. Structures found solely in either humans or macaques are identified. The purpose of the NeuroNames Brain Hierarchy is to bring greater standardization to the neuroanatomical terminology used by scientific investigators, clinicians, and students. This effort is consistent with the goals of the Unified Medical Language System program of the National Library of Medicine. It is hoped that the systematic construction of the NeuroNames Brain Hierarchy will facilitate use of the most widely accepted definitions of classical neuroanatomy in quantitative computerized neuroimaging applications. It should provide an accurate structural framework against which to reference the many other kinds of neuroanatomical information that are acquired by modern imaging, mapping, and histological labeling techniques.

Animals↗

PubMed1994

Global information infrastructure.

The High Performance Computing and Communications Program (HPCC) is a multiagency federal initiative under the leadership of the White House Office of Science and Technology Policy, established by the High Performance Computing Act of 1991. It has been assigned a critical role in supporting the international collaboration essential to science and to health care. Goals of the HPCC are to extend USA leadership in high performance computing and networking technologies; to improve technology transfer for economic competitiveness, education, and national security; and to provide a key part of the foundation for the National Information Infrastructure. The first component of the National Institutes of Health to participate in the HPCC, the National Library of Medicine (NLM), recently issued a solicitation for proposals to address a range of issues, from privacy to 'testbed' networks, 'virtual reality,' and more. These efforts will build upon the NLM's extensive outreach program and other initiatives, including the Unified Medical Language System (UMLS), MEDLARS, and Grateful Med. New Internet search tools are emerging, such as Gopher and 'Knowbots'. Medicine will succeed in developing future intelligent agents to assist in utilizing computer networks. Our ability to serve patients is so often restricted by lack of information and knowledge at the time and place of medical decision-making. The new technologies, properly employed, will also greatly enhance our ability to serve the patient.

Computer Communication Networks↗

PubMed1992

Integrating the UMLS into VNS Retriever.

We are developing a networked resource for the National Library of Medicine's Unified Medical Language System. We call this resource the UMLS Retriever, which is an instance of our VNS Retriever architecture. Our prototype user interface makes use of the Virtual Notebook System Browser. The development of a networked UMLS service will result in numerous advantages to our user community.

Computer Communication Networks↗