PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Vocabulary, Controlled”

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 55 records · Page 3Linked to original sources

The data dictionary--a controlled vocabulary for integrating clinical databases and medical knowledge bases.

The medical information systems of the future will probably include the entire medical record as well as a knowledge base, providing decision support for the physician during patient care. Data dictionaries will play an important role in integrating the medical knowledge bases with the clinical databases. This article presents an infological data model of such an integrated medical information system. Medical events, medical terms, and medical facts are the basic concepts that constitute the model. To allow the transfer of information and knowledge between systems, the data dictionary should be organized with regard to several common classification schemes of medical nomenclature.

Database Management Systems↗

How useful is the UMLS metathesaurus in developing a controlled vocabulary for an automated problem list?

We are developing a set of problem list phrases to be used in the automated problem list of a prototype clinical computing system. Because of the large number of terms in the Unified Medical Language System (UMLS) and the links between them, we are experimenting with the use of the UMLS as the foundation for our problem list phrase set. We have found the UMLS to be very useful for this project, but that it lacks many phases clinicians wish to include in the problem list. Internal linkages between phrases provided in the UMLS are not well suited to our needs. We plan to continue our use of the UMLS but to add problem list phrases and linkages between phrases to support browsing and decision support applications.

Ambulatory Care Information Systems↗

Formal descriptions and adaptive mechanisms for changes in controlled medical vocabularies.

Standard controlled medical vocabularies are typically based on a coding scheme, while medical informatics applications tend to have a more formal conceptual foundation. When such applications attempt to use data coded with standard vocabularies, problems can arise when the standard vocabulary changes over time. A formal taxonomy is presented for describing the semantic changes which can occur in a vocabulary, such as simple addition, refinement, precoordination, disambiguation, redundancy, obsolescence, discovered redundancy, major name changes, minor name changes, code reuse, and changed codes. The taxonomy is described that used to effect change in one concept-based vocabulary (the Medical Entities Dictionary), and the utility of the approach is demonstrated by applying it to the changes appearing in the 1994 release of the International Classification of Diseases, Ninth Edition, with Clinical Modifications (ICD-9-CM).

Classification↗

A concept model for the automatic maintenance of controlled medical vocabularies.

A controlled medical vocabulary is a fundamental requirement in a range of medical informatics applications. Large vocabularies development and maintenance is labor intensive and costly. Maintainers of medical vocabularies need appropriate tools to do their work correctly. In this paper, we describe our concept model for a controlled medical vocabulary. We present how this model can check vocabulary consistency. We propose a set of tools in a distributed environment, which permits edition, visualization and maintenance of medical terminologies.

Humans↗

Development of a change model for a controlled medical vocabulary.

Managing change in controlled medical vocabularies is labor intensive and costly, but change is inevitable if vocabularies are to be kept up to date. The changes that are appropriate for a controlled medical vocabulary depend on the data stored for that vocabulary, and those data in turn depend on the needs of users. The set of change operations is the change model; the data stored about concepts comprise the concept model. Because the change model depends directly on the concept model, a discussion of the former necessitates a discussion of the latter. In this paper, we first present a set of tasks that we believe controlled medical vocabularies should handle. Next, we describe our concept model for a controlled medical vocabulary. Then, we review the literature on changes in existing vocabulary systems. Finally, we present our change model. We call our system, which incorporates the concept model and change model, the General Online Dictionary of Medicine (GOLDMINE).

Models, Theoretical↗

Guideline and quality indicators for development, purchase and use of controlled health vocabularies.

Developers and purchasers of controlled health terminologies require valid mechanisms for comparing terminological systems. By Controlled Health Vocabularies, we refer to terminologies and terminological systems designed to represent clinical data at a granularity consistent with the practice of today's healthcare delivery. Comprehensive criterion for the evaluation of such systems historically have been lacking and the known criteria are inconsistently applied. Although there are many papers which describe specific desirable features of a controlled health vocabulary, to date there is not a consistent guide for evaluators of terminologies to reference, which will help them compare implementations of terminological systems on an equal footing [Methods Inf. Med. 37 (1998) 394, J. Am. Med. Inform. Assoc. 5 (1998) 503]. This guideline serves to fill the gap between academic enumeration of desirable terminological characteristics and the practical implementation or rigorous evaluations which will yield comparable data regarding the quality of one or more controlled health vocabularies.

Guidelines as Topic↗

Controlling the vocabulary for anatomy.

When confronted with the representation of human anatomy, natural language processing (NLP) system designers are facing an unsolved and frequent problem: the lack of a suitable global reference. The available sources in electronic format are numerous, but none fits adequately all the constraints and needs of language analysis. These sources are usually incomplete, difficult to use or tailored to specific needs. The anatomist's or ontologist's view does not necessarily match that of the linguist. The purpose of this paper is to review most recognized sources of knowledge in anatomy usable for linguistic analysis. Their potential and limits are emphasized according to this point of view. Focus is given on the role of the consensus work of the International Federation of Associations of Anatomists (IFAA) giving the Terminologia Anatomica.

Anatomy↗

A controlled nursing vocabulary for indexing and information retrieval.

The lack of a nursing thesaurus in Finnish has emerged among nursing professionals searching nursing knowledge and librarians when indexing literature to databases. The Finnish Nursing Education Society launched a project focusing on the development of a nursing vocabulary and the compilation of a thesaurus. The content of a vocabulary was created by six experts using Delphi-technique. The validity of the vocabulary was twice tested for indexing nursing research and has afterwards been revised. The vocabulary can be used for indexing and information retrieval purposes. The main challenge is that nurses easily can find national as well as international nursing research from databases and enhance research utilization.

Abstracting and Indexing↗

Empirical derivation of an electronic clinically useful problem statement system.

Problem lists are tools to improve patient management. In the medical record, they connect diagnoses to therapy, prognosis, and psychosocial issues. Computer-based problem lists enhance paper-based approaches by enabling cost-containment and quality assurance applications, but they require clinically expressive controlled vocabularies. Because existing controlled vocabularies do not represent problem statements at a clinically useful level, we derived a new canonical problem statement vocabulary through semi-automated analysis and distillation of provider-entered problem lists collected over 6 years from 74,696 patients. We combined automated and manual methods to condense 891,770 problem statements entered by 1961 care providers at Grady Memorial Hospital in Atlanta, Georgia, to 15,534 Canonical Clinical Problem Statement System (CCPSS) terms. The nature and frequency of problem statements were characterized, interrelations among them were enumerated, and a database capturing the epidemiology of problems was created. The authors identified 23,503 problem relations (co-occurrences, sign-symptom complexes, and differential diagnoses) and 22,690 modifier words that further categorized "canonical" problems. To assess completeness, CCPSS content was compared with that of the 1997 Unified Medical Language System Metathesaurus (containing terms from 44 clinical vocabularies). Unified Medical Language System terms expressed 25% of individual CCPSS terms exactly (71% of problems by frequency), 27% partially, and 48% poorly or not at all. Clinicians judged that CCPSS terms completely captured their clinical intent for 84% of 686 randomly selected free-text problem statements. The CCPSS represents clinical concepts at a level exceeding that of previous approaches. A similar national approach could create a standardized, useful, shared resource for clinical practice.

Humans↗

Barriers to the clinical implementation of compositionality.

BACKGROUND: Compositional mechanisms for the entry of clinically relevant controlled vocabularies have been suggested as a possible solution to providing adequate descriptive precision while keeping term vocabulary redundancy under control. As of yet, there are no widely accepted term navigators that allow physicians to enter problem lists utilizing controlled vocabularies with compositionality. METHODS: We report on the results of a usability trial of 5 physicians using our most recent attempt at developing the Mayo Problem List Manager. We tested the implementation of an automated term composition, and hierarchical term dissection. RESULTS: Participants found acceptable terms 96% of the time and found automated term composition helpful in 85% of the case scenarios. There was significant confusion about the terminology used to describe compositional elements (kernel concepts, modifiers, and qualifiers) however participants used the functions appropriately. Speed of entry was universally stated as the limiting factor. CONCLUSIONS: The variety of methods that our participants used to enter terms highlights the need for multiple ways to accomplish the task of data entry. Successful implementation of user directed compositionality could be accomplished with further improvement of the user interface and the underlying terminology.

Humans↗

Building a controlled health vocabulary in Japanese.

OBJECTIVES: This study is aimed at developing a controlled clinical vocabulary for use in electronic patient record (EPR) systems. METHODS: In this paper, we propose a model for building the vocabulary. The model is composed of a Canonical Term Dictionary, an Atom Dictionary, a Composite Atom Dictionary, and an Index. Parsing and composing functions are included in this model. Canonical terms were extracted from reference terminologies. Atoms were extracted from the Canonical Term Dictionary and reduced to a set from which the Composite Atom Dictionary can be built. The index was built to link these two dictionaries. For testing the model, we compiled a sample vocabulary and applied the model to a SNOMED translation system (English to Japanese) and a term similarity estimation system. RESULTS: The sample vocabulary consisted of 15,600 atomic terms and 4,450 composite terms. 33,441 SNOMED terms were translated by the SNOMED translation system. The system gave adequate Japanese candidates in 56.3% of cases. The similarity estimation system found an average of 5.4 candidates when the equality ratio was over 50%. CONCLUSIONS: The trial applications produced good results. The model seems promising for building a standard clinical vocabulary system. This system can be applied in certain other Asian countries, such as China and Korea.

Algorithms↗

Guideline for health informatics: controlled health vocabularies--vocabulary structure and high-level indicators.

Developers and purchasers of controlled health terminologies require valid mechanisms for comparing terminological systems. By Controlled Health Vocabularies we refer to terminologies and terminological systems designed to represent clinical data at a granularity consistent with the practice of today's healthcare delivery. Comprehensive criterion for the evaluation of such systems are lacking and the known criteria are inconsistently applied. Although there are many papers, which describe specific desirable features of a controlled health vocabulary, to date there is not a consistent guide for evaluators of terminologies to reference, which will help them compare implementations of terminological systems on an equal footing 1,2 This guideline serves to fill the gap between academic enumeration of desirable terminological characteristics and the practical implementation or rigorous evaluations which will yield comparable data regarding the quality of one or more controlled health vocabularies.

Medical Informatics↗

caCORE: a common infrastructure for cancer informatics.

MOTIVATION: Sites with substantive bioinformatics operations are challenged to build data processing and delivery infrastructure that provides reliable access and enables data integration. Locally generated data must be processed and stored such that relationships to external data sources can be presented. Consistency and comparability across data sets requires annotation with controlled vocabularies and, further, metadata standards for data representation. Programmatic access to the processed data should be supported to ensure the maximum possible value is extracted. Confronted with these challenges at the National Cancer Institute Center for Bioinformatics, we decided to develop a robust infrastructure for data management and integration that supports advanced biomedical applications. RESULTS: We have developed an interconnected set of software and services called caCORE. Enterprise Vocabulary Services (EVS) provide controlled vocabulary, dictionary and thesaurus services. The Cancer Data Standards Repository (caDSR) provides a metadata registry for common data elements. Cancer Bioinformatics Infrastructure Objects (caBIO) implements an object-oriented model of the biomedical domain and provides Java, Simple Object Access Protocol and HTTP-XML application programming interfaces. caCORE has been used to develop scientific applications that bring together data from distinct genomic and clinical science sources. AVAILABILITY: caCORE downloads and web interfaces can be accessed from links on the caCORE web site (http://ncicb.nci.nih.gov/core). caBIO software is distributed under an open source license that permits unrestricted academic and commercial use. Vocabulary and metadata content in the EVS and caDSR, respectively, is similarly unrestricted, and is available through web applications and FTP downloads. SUPPLEMENTARY INFORMATION: http://ncicb.nci.nih.gov/core/publications contains links to the caBIO 1.0 class diagram and the caCORE 1.0 Technical Guide, which provide detailed information on the present caCORE architecture, data sources and APIs. Updated information appears on a regular basis on the caCORE web site (http://ncicb.nci.nih.gov/core).

Animals↗

Pathbase: a new reference resource and database for laboratory mouse pathology.

Pathbase (http://www.pathbase.net) is a web accessible database of histopathological images of laboratory mice, developed as a resource for the coding and archiving of data derived from the analysis of mutant or genetically engineered mice and their background strains. The metadata for the images, which allows retrieval and interoperability with other databases, is derived from a series of orthogonal ontologies and controlled vocabularies. One of these controlled vocabularies, MPATH, was developed by the Pathbase Consortium as a formal description of the content of mouse histopathological images. The database currently has over 1000 images on-line with 2000 more under curation and presents a paradigm for the development of future databases dedicated to aspects of experimental biology.

Animals↗

Applying the desiderata for controlled medical vocabularies to drug information databases.

Medication history has always been an integral part of the patient's medical record. With the advent of the computerized medical record and the longitudinal clinical data repository, having the medication history has enabled the development of clinical decision support system that alerts for drug to drug interactions and drug allergies. Furthermore, medication data is increasingly being analyzed from a utilization and clinical outcomes standpoint. For these activities to occur, a controlled pharmacy vocabulary akin to a controlled medical vocabulary is essential. Drug information databases are well-established sources of information for pharmacy-related data and products. However, do they measure up as a controlled vocabulary? Recent experience reviewing drug information databases and integrating pharmacy-related information into a data dictionary in real-time clinical use at multiple health care institutions have revealed several challenges and issues. These are discussed according to Cimino's desiderata for controlled medical vocabularies.

Databases, Factual↗

Using digrams to map controlled medical vocabularies.

A program for matching between controlled medical vocabularies has been developed which adopts methods used in the domain of Information Retrieval. This program combines a stemmer based on fragments of words (digrams) with a similarity function. The proposed stemmer did not require any knowledge about word-formation rules and helped the identification of several kinds of word variants. The adopted similarity function assigned the highest score to the best candidate match in 99.0% of the cases.

Algorithms↗

Desiderata for controlled medical vocabularies in the twenty-first century.

Builders of medical informatics applications need controlled medical vocabularies to support their applications and it is to their advantage to use available standards. In order to do so, however, these standards need to address the requirements of their intended users. Over the past decade, medical informatics researchers have begun to articulate some of these requirements. This paper brings together some of the common themes which have been described, including: vocabulary content, concept orientation, concept permanence, nonsemantic concept identifiers, polyhierarchy, formal definitions, rejection of "not elsewhere classified" terms, multiple granularities, multiple consistent views, context representation, graceful evolution, and recognized redundancy. Standards developers are beginning to recognize and address these desiderata and adapt their offerings to meet them.

Humans↗

Mapping clinically useful terminology to a controlled medical vocabulary.

We have mapped clinically used diagnostic terms from a legacy ambulatory care system to the separate controlled vocabulary of our central clinical information system. The methodology combines elements of lexical and morphologic text matching techniques, followed by manual physician review. Results of the automated matching algorithm before and after partial manual review are presented. The results of this effort will permit the migration of coded clinical data from one system to another. Output from the system after the term review process will be fed back to the target vocabulary via automated and semi-automated means to improve its clinical utility.

Academic Medical Centers↗