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PubMed2005

Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary.

MOTIVATION: High-throughput technologies such as DNA sequencing and microarrays have created the need for automated annotation of large sets of genes, including whole genomes, and automated identification of pathways. Ontologies, such as the popular Gene Ontology (GO), provide a common controlled vocabulary for these types of automated analysis. Yet, while GO offers tremendous value, it also has certain limitations such as the lack of direct association with pathways. RESULTS: We demonstrated the use of the KEGG Orthology (KO), part of the KEGG suite of resources, as an alternative controlled vocabulary for automated annotation and pathway identification. We developed a KO-Based Annotation System (KOBAS) that can automatically annotate a set of sequences with KO terms and identify both the most frequent and the statistically significantly enriched pathways. Results from both whole genome and microarray gene cluster annotations with KOBAS are comparable and complementary to known annotations. KOBAS is a freely available stand-alone Python program that can contribute significantly to genome annotation and microarray analysis.

Artificial Intelligence↗
PubMed2005

Using Gene Ontology and genomic controlled vocabularies to analyze high-throughput gene lists: three tool comparison.

In genomic and molecular biology domains, controlled vocabularies and ontologies are becoming of paramount importance to integrate and correlate the massive amount of information increasingly accumulating in heterogeneous and distributed databanks. Although at present they are still few and present some issues, they can effectively be used also to biologically annotate genes on a genomic scale and across different species, and to evaluate the relevance of such annotations. Here, we compare three tools using the Gene Ontology and genomic controlled vocabularies to statistically highlight significant biological characteristics of gene sets to help in the biological interpretation of high-throughput experiment results and knowledge discovery from data.

Computational Biology↗
PubMed1997

System design challenges: the integration of controlled vocabulary use into daily practice.

System developers face the challenge of providing tools to improve health care productivity and quality. While progress is being made, clearly defined requirements, standards and development guidelines are not yet complete enough to provide the details needed to build such software. We have moved forward and created a clinical documentation system using a controlled vocabulary. Doctors and nurses use data entry tools such as browsers and templates to access the vocabulary for creating clinical notes. Our library of terms is mapped to SNOMED which is not complete enough to support clinical documentation. Therefore our libraries have been expanded beyond SNOMED. Overall we have found that clinicians will use a controlled vocabulary when it is intuitive, predictable, flexible, complete and fast to use. Other benefits have surfaced such as more complete and consistent documentation. Our experiences to date confirm that there is still much to explore, discover, invent and create.

Attitude to Computers↗
PubMed2003

Matching controlled vocabulary words.

This study examines an enabling condition for natural languages access to medical knowledge resources (Medline, CISMeF) indexed with controlled vocabularies (e.g., the MeSH): is the vocabulary of user queries comparable with that of the index terms? The two vocabularies were compared in their original form, then under incrementally normalized forms, using character-based normalizations then linguistic normalizations. Only 16.7% of the user vocabulary, in its original form, is in the MeSH. Progressive normalizations increase this proportion to 65.5%. Besides, if the frequencies of occurrence of words are taken into account, 89.3% of user word occurrences can be matched to MeSH words. This shows the interest of taking into account further matching methods between queries and index terms than those presented here.

France↗
PubMed2000

Developing a controlled vocabulary for use in a hospital information system.

In bringing a controlled vocabulary to a health information system, it is important to include those terms commonly used by those who must routinely input data to the system. We have developed a methodology whereby we can obtain "free text" descriptions of diagnoses entered by system users. We then sort those terms/concepts by system and find the appropriate "atomic" term(s). The terms are also being submitted to domain experts for appropriateness and fidelity. These concepts are then coded in an international coding system (SNOMED International) to eventually be entered into the controlled "pick list" of terms available for users to enter.

Animals↗
PubMedDate not supplied

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↗
PubMed1995

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↗
PubMed2001

A controlled vocabulary for nursing and allied health in Norway.

Nursing and allied health libraries at educational institutions in Norway have generally indexed their book collections with uncontrolled terms. With the reorganization of higher education in 1994, the majority of these libraries joined BIBSYS, which is a joint library system for higher education and research in Norway. This has led to chaos when searching the joint catalogue for literature on nursing and related fields. A term such as 'behaviour problems' may have up to five synonyms. In an attempt to improve the quality of searching the health literature, BIBSYS appointed a working group in the Spring of 1999 to find a suitable controlled vocabulary for this subject area, and to see how this vocabulary could be integrated into BIBSYS. The group presented its recommendations in October 1999. The report has been well received by the BIBSYS Board and by user groups. There are no Norwegian vocabularies that are suitable for use in nursing and allied health, therefore it will be necessary to translate and combine existing thesauri. The group has looked at the Nordic Multilingual Thesaurus on Health Promotion, the Swedish Spriline Thesaurus, MeSH (Medical Subject Headings) and CINAHL Subject Heading List. Other relevant thesauri are AMED/CATS Thesaurus, Bioethics Thesaurus (Bioethicsline) and the RCN thesaurus. The group recommends the development of a Norwegian thesaurus based on a translation of parts of MeSH and CINAHL Subject Heading List.

Abstracting and Indexing↗
PubMed2002

A controlled vocabulary for nursing practice and research.

The paper describes the process of evolving a controlled Nursing Vocabulary for indexing and information retrieval purposes. The vocabulary is based on terms of the thesaurus of Medline i.e. MeSH (Medical Subject Headings) by National Library of Medicine and the International Classification for Nursing Practice (ICNP) by the International Council of Nurses. The vocabulary consists of 10 themes central to nursing domain. The vocabulary is added as an independent theme to the Finnish thesaurus FinMeSH. The validity of the vocabulary has been tested for indexing nursing research once and has afterwards been revised. The validity will be tested again during spring 2002.

Finland↗
PubMed1991

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↗
PubMed1997

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↗
PubMed2003

Enhancing evidence-based practice--a controlled vocabulary for nursing practice and research.

Evidence-based practice requires flexible systems for information retrieval. Search processes in electronic databases must be based on terms, which are familiar to professionals and describe accurately the problem to be solved. The paper describes the process of evolving a controlled nursing vocabulary for indexing and information retrieval purposes. The vocabulary is based on terms of the Medline thesaurus the medical subject headings (MeSH) by National Library of Medicine and the International Classification for Nursing Practice by the International Council of Nurses. The created vocabulary consists of 10 themes central to nursing domain. The content was compiled using Delphi technique. The expert panel had four rounds and at the first stage the vocabulary consists of 558 terms. The validity of the vocabulary has been tested for indexing nursing research and based on the validation the expert panel has made some changes for the hierarchy and content. The vocabulary will be added as an independent theme to the Finnish thesaurus the FinMeSH.

Clinical Nursing Research↗
PubMed1995

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↗
PubMed2003

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↗
PubMed2000

Issues in designing a controlled vocabulary and a patient data representation model for a hospital-wide EPR system.

Careful attention must be paid when designing the following components of a hospital-wide electronic patient record (EPR) system: the common medical controlled terminology, the semantic data model for a standardized representation of patient data and the normalized database. Requirements are detailed. Problems encountered when coordinating the development of a medical terminology with the modeling of patient medical data are analyzed. Solutions implemented in the hospital-wide DOCLIN EPR system at the University Hospital of Geneva (HUG) are presented.

Computer Communication Networks↗
PubMed2002

Variations in Medical Subject Headings (MeSH) mapping: from the natural language of patron terms to the controlled vocabulary of mapped lists.

OBJECTIVES: This study compared the mapping of natural language patron terms to the Medical Subject Headings (MeSH) across six MeSH interfaces for the MEDLINE database. METHODS: Test data were obtained from search requests submitted by patrons to the Library of the Health Sciences, University of Illinois at Chicago, over a nine-month period. Search request statements were parsed into separate terms or phrases. Using print sources from the National Library of Medicine, Each parsed patron term was assigned corresponding MeSH terms. Each patron term was entered into each of the selected interfaces to determine how effectively they mapped to MeSH. Data were collected for mapping success, accessibility of MeSH term within mapped list, and total number of MeSH choices within each list. RESULTS: The selected MEDLINE interfaces do not map the same patron term in the same way, nor do they consistently lead to what is considered the appropriate MeSH term. CONCLUSIONS: If searchers utilize the MEDLINE database to its fullest potential by mapping to MeSH, the results of the mapping will vary between interfaces. This variance may ultimately impact the search results. These differences should be considered when choosing a MEDLINE interface and when instructing end users.

Humans↗