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Electronic surveillance of the pharmacology-toxicology literature: the need for controlled vocabularies and registry systems.

Controlled vocabularies of "preferred names" and registry systems are essential in electronic indexing, storing, searching, and retrieving the world's published literature. The most efficient and comprehensive search is accomplished by using the preferred name. Without a controlled vocabulary or a registry system, it would be necessary to remember every name that might have been used by authors since January 1966, in order to retrieve all the citations on a chemical from over 7.8 million citations currently in the National Library of Medicine's MEDLINE and its backfiles. The task of creating the list of subject descriptors that make possible the surveillance of published literature via electronic databases requires the participation of the scientific community in developing domain-specific nomenclature, drug classification, controlled vocabularies, and registry systems as well. The biological unions of the International Council of Scientific Unions and its Committee on Data for Science and Technology are major contributors to the establishment and dissemination of standards for biological terminology and nomenclature. The objectives of the IUPHAR Nomenclature Committee include the development of a rational framework for the nomenclature of receptor classes or families and a classification for therapeutic agents. This will help define rules for the characterization and classification of receptors that are stable and easy to comprehend. The International Union of Pharmacology publishes guidelines for the classification of drugs and the nomenclature of receptors and ion channels.

Abstracting and Indexing

Coping with changing controlled vocabularies.

For the foreseeable future, controlled medical vocabularies will be in a constant state of development, expansion and refinement. Changes in controlled vocabularies must be reconciled with historical patient information which is coded using those vocabularies and stored in clinical databases. This paper explores the kinds of changes that can occur in controlled vocabularies, including adding terms (simple additions, refinements, redundancy and disambiguation), deleting terms, changing terms (major and minor name changes), and other special situations (obsolescence, discovering redundancy, and precoordination). Examples are drawn from actual changes appearing in the 1993 update to the International Classification of Diseases (ICD9-CM). The methods being used at Columbia-Presbyterian Medical Center to reconcile its Medical Entities Dictionary and its clinical database are discussed.

Medical Records Systems, Computerized

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

microntology: a lightweight, data-driven controlled vocabulary to describe earth's microbial habitats.

MOTIVATION: Data-enabled studies of microbial ecology and evolution depend on high-quality descriptions of microbial habitats, based on curated and consolidated vocabularies. RESULTS: We introduce microntology v1.0, a pragmatic controlled vocabulary of 148 terms to describe microbial habitats and lifestyles, and provide manually curated microntology annotations for >300k metagenomic samples from public repositories. AVAILABILITY: microntology controlled vocabulary terms and term hierarchies (doi: 10.5281/zenodo.19730167), and curated annotations for 305 626 metagenomic samples (doi: 10.5281/zenodo.18164252) are available via Zenodo and spire.embl.de/downloads. Underlying code is available via github.com/grp-schmidt/microntology and Zenodo (doi: 10.5281/zenodo.20323497). User feedback, suggestions and bug reports are welcome at github.com/grp-schmidt/microntology/issues.

Ecosystem

The Australian Medicines Handbook and its controlling vocabularies.

The Australian Medicines Handbook is intended to serve as an independent peer-reviewed knowledge resource for health care providers. A printed handbook is planned first, followed by electronic products. Information about medication will be dissected and entered into a detailed database, whose elemental nature should make it "computer understandable." The terms used in the database will be cross-referenced to preferred terms. Thus, the vocabulary will be controlled. The handbook will be printed directly from the database with use of database publishing techniques.

Australia

The PEN-Ivory project: exploring user-interface design for the selection of items from large controlled vocabularies of medicine.

OBJECTIVE: To explore different user-interface designs for structured progress note entry, with a long-term goal of developing design guidelines for user interfaces where users select items from large medical vocabularies. DESIGN: The authors created eight different prototypes of a pen-based progress-note-writing system called PEN-Ivory. Each prototype allows physicians to write patient progress notes using simple pen-based gestures such as circle, line-out, and scratch-out. The result of an interaction with PEN-Ivory is a progress note in English prose. The eight prototypes were designed in a principled way, so that they differ from one another in just one of three different user-interface characteristics. MEASUREMENTS: Five of the eight prototypes were tested by measuring the time it took 15 users, each using a distinct prototype, to document three patient cases consisting of a total of 63 medical findings. RESULTS: The prototype that allowed the fastest data entry had the following three user-interface characteristics: it used a paging rather than a scrolling form, it used a fixed palette of modifiers rather than a dynamic "pop-up" palette, and it made available all findings from the controlled vocabulary at once rather than displaying only a subset of findings generated by analyzing the patient's problem list. CONCLUSION: Even simple design changes to a user interface can make dramatic differences in user performance. The authors discuss possible influences on performance, such as positional constancy, user uncertainty and system anticipation, that may contribute significantly to the effectiveness of systems that display menus of items from large controlled vocabularies of medicine.

Medical Records

Implementing a low-cost computer-based patient record: a controlled vocabulary reduces data base design complexity.

In order to build a computer-based patient record (CPR) system suitable for use in solo and small group practice settings it is necessary to use development methods that minimize cost. Design complexity is a major source of high cost. Reducing complexity should result in lower development, deployment and maintenance costs as well as higher reliability. We have developed a simplified relational model and have used that model, in conjunction with a controlled vocabulary, to implement a CPR that can capture and store patient examinations and other forms of clinical notes as well as laboratory and other test results. The information can be viewed in a familiar document format and it can accessed for other types of processing using standard Structured Query Language (SQL) techniques. The database, as implemented, uses inexpensive components resulting in a system that is not prohibitively expensive for solo practitioners and small groups. In addition the architecture is scaleable and can accommodate very large numbers of patients and practitioners.

Computer Systems

Object-oriented controlled-vocabulary translator using TRANSOFT + HyperPAD.

Automated coding of surgical pathology reports is demonstrated. This public-domain translation software operates on surgical pathology files, extracting diagnoses and assigning codes in a controlled medical vocabulary, such as SNOMED. Context-sensitive translation algorithms are employed, and syntactically correct diagnostic items are produced that are matched with controlled vocabulary. English-language surgical pathology reports, accessioned over one year at the Baltimore Veterans Affairs Medical Center, were translated. With an interface to a larger hospital information system, all natural language pathology reports are automatically rendered as topography and morphology codes. This translator frees the pathologist from the time-intensive task of personally coding each report, and may be used to flag certain diagnostic categories that require specific quality assurance actions.

Algorithms

Controlled vocabulary and design of laboratory results displays.

Traditional data-review displays are driven by the ancillary systems that produced the data. A different paradigm is being used at Columbia-Presbyterian Medical Center (CPMC) where a controlled medical vocabulary-the Medical Entities Dictionary (MED) is the driving force behind laboratory data-review displays. Using hierarchical and semantic networks the authors have constructed a Web-based tool that considerably simplifies the MED-editing task required to create new displays. The tool uses knowledge in the MED to extract contextually relevant hierarchic and semantic sub-nets from the MED. The tool has a sensitivity of 92.2% and a relevance of 94.7% for retrieval of terms from the MED. Based on these results and given sufficient domains' structure within controlled vocabularies, we conclude that similar algorithms will enable applications to design and generate customized displays on-the-fly.

Algorithms

Coding systems and controlled vocabularies for hospital information systems.

Modern healthcare information systems are requested to support an increasing interaction among professionals (inside and across the borders of the hospital) and a growing integration of specialised tasks (provision of care, reimbursement, document retrieval, optimisation of resource use, clinical audit, etc.). Coding systems were conceived and optimised independently for various specific purposes. They are now facing each other and thus conflicting into this new environment; the solution will be in a more application-independent representation of concepts. Developments are going towards three complementary directions: (i) to separate different functions about the management of terms and concepts, and thus to produce more specialised software components; (ii) to develop a new class of software which is able to manage terminological diversity without imposing uniformity; and (iii) to enhance reusability of concepts, and facilitate a spontaneous convergence among controlled vocabularies.

Artificial Intelligence

eVOC: a controlled vocabulary for unifying gene expression data.

Expression data contribute significantly to the biological value of the sequenced human genome, providing extensive information about gene structure and the pattern of gene expression. ESTs, together with SAGE libraries and microarray experiment information, provide a broad and rich view of the transcriptome. However, it is difficult to perform large-scale expression mining of the data generated by these diverse experimental approaches. Not only is the data stored in disparate locations, but there is frequent ambiguity in the meaning of terms used to describe the source of the material used in the experiment. Untangling semantic differences between the data provided by different resources is therefore largely reliant on the domain knowledge of a human expert. We present here eVOC, a system which associates labelled target cDNAs for microarray experiments, or cDNA libraries and their associated transcripts with controlled terms in a set of hierarchical vocabularies. eVOC consists of four orthogonal controlled vocabularies suitable for describing the domains of human gene expression data including Anatomical System, Cell Type, Pathology and Developmental Stage. We have curated and annotated 7016 cDNA libraries represented in dbEST, as well as 104 SAGE libraries,with expression information,and provide this as an integrated, public resource that allows the linking of transcripts and libraries with expression terms. Both the vocabularies and the vocabulary-annotated libraries can be retrieved from http://www.sanbi.ac.za/evoc/. Several groups are involved in developing this resource with the aim of unifying transcript expression information.

Animals

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

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

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

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