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PubMed1984

BIOETHICSLINE: an overview for searchers.

The BIOETHICSLINE File (BIOETHICS) is a bibliographic database produced at the Kennedy Institute of Ethics, Georgetown University, and made available online through the the National Library of Medicine's MEDLARS system. BIOETHICS provides multidisciplinary coverage of the ethical, legal, and public policy aspects of medicine, health care, and biomedical and behavioral research. This article describes the content and development of the database, suggests effective strategies for searching, and presents illustrative searches. An appendix lists selected major indexing terms (Keywords) from the controlled vocabulary of the Bioethics Thesaurus.

Bioethical Issues↗

PubMed2007

Proteomic data exchange and storage: the need for common standards and public repositories.

The ever increasing volumes of proteomic data now being produced by laboratories across the world have resulted in major issues in data storage and accessibility. The further demands of multilaboratory initiatives has highlighted issues when collaborators cannot import data generated within the same project but generated by different hardware types and processed by laboratory-specific work flows and analyses packages. There is an increasing need for common data standards that will allow the interchange of data between different instrumentation, search engines, and between laboratory databases. This could then lead to the establishment of data repositories from where benchmark datasets could be accessed and reanalyzed. The Human Proteome Organization is currently supporting efforts to establish such standards. The work of the Proteomics Standards Initiative has lead to the development of the mzData XML interchange standard and is now broadening its scope to produce a spectral analysis output format, mzIdent. Accompanying controlled vocabularies allow the accurate, while systematic, representation of metadata throughout both schema.

Benchmarking↗

PubMed2005

A system of metadata to control the process of query, aggregating, cleaning and analysing large datasets of primary care data.

BACKGROUND: Metadata is data that describes other data or resources. It has a defined number of named elements that convey meaning. Medical data are complex to process. For example, in the Primary Care Data Quality (PCDQ) renal programme, we need to collect over 300 variables because there are so many possible causes of renal disease. These variables are not just single columns of data--all are extracted as code plus date, while others are code-date-value. Metadata has the potential to improve the reliability of processing large datasets. OBJECTIVE: To define unique and unambiguous metadata headings for clinical data and derived variables. METHOD: We defined the look-up tables we would use as a controlled vocabulary to name the core clinical concepts within the metadata. We added six other elements to describe data: (1) the study or audit name; (2) the query used to extract the data; (3) the data collection number; (4) the type of data, including specifying the units; (5) the repeat number (if the variable was extracted more than once); and (6) a processing suffix that defines how the data have been processed. RESULTS: The metadata system has enabled the development of a query library and an analysis syntax library that make data processing and analysis more efficient. Its stability means greater effort can be put into more complex data processing, and some semiautomation of processes. However, the system has had implementation problems. It has been particularly hard to stop clinicians using multiple synonyms for the same variable. CONCLUSIONS: The PCDQ metadata system provides an auditable method of data processing. It is a method that should improve the reliability, validity and efficiency of processing routinely collected clinical data. This paper sets out to demystify our data processing method and makes the PCDQ metadata system available to clinicians and data processors who might wish to adopt it.

Electronic Data Processing↗

PubMed2005

[The use of bibliographic databases by Spanish-speaking Latin American biomedical researchers: a cross-sectional study].

OBJECTIVE: To describe how Spanish-speaking biomedical professionals in Latin America access and utilize bibliographic databases. METHODS: Based on a MEDLINE search, 2 515 articles published between August 2002 and August 2003 were identified that dealt with and/or had authors from 16 countries: Argentina, Bolivia, Chile, Colombia, Costa Rica, Cuba, Ecuador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay, and Venezuela. The search was limited to references to basic science, clinical science, or social medicine. A survey was sent by e-mail to researchers who lived in 15 of the 16 countries (the exception being Nicaragua). The survey asked about the researcher's area of work (basic science, clinical science, or public health), the level of skill in using databases, the frequency and type of access to the databases most utilized, the impact from not having access to the full text of articles when preparing a manuscript, and how the respondent usually obtained the full-text version of articles. RESULTS: A total of 586 e-mail messages with the survey were sent out, and 185 responses were received (32%). The databases most utilized to obtain biomedical information were MEDLINE (34.1%), general search engines (Google, Yahoo!, and AltaVista) (15.9%), on-line journals (9.8%), BIREME-LILACS (6.0%), BioMedNet (5.4%), the databases of the Centers for Disease Control and Prevention of the United States of America (5.2%), and the Cochrane Library (4.9%). Of the respondents, 64% said they had average or advanced abilities in using MEDLINE. However, 71% of the respondents did not use or were not aware of the MEDLINE Medical Subject Headings (MeSH), a controlled vocabulary established by the National Library of Medicine of the United States of America for indexing articles. The frequency of accessing the databases was similar in all the countries studied, without significant differences in terms of the type of access (authorized access to commercial databases, unauthorized access to those databases, or access to databases available for free) or the level of abilities. Of the respondents, 87% said they had not included important references in the articles that they had published because they had not had access to the full text of those items, and 56% said they had cited articles that they had not read in full. In addition, 7.6% of the respondents admitted to unauthorized use of limited-access databases, such as through borrowed passwords or copied disks. More than two-thirds of the respondents said they obtained the full text of articles through photocopies or directly from the authors. CONCLUSIONS: In order to encourage scientific output by Latin American researchers, more of them need to be trained in the use of the most frequently used databases, especially MEDLINE. Those researchers also need to have expanded access to the biomedical literature.

Biomedical Research↗

PubMedDate not supplied

Decision support for drug prescription integrated with computer-based patient records in primary care.

A conceptual model of an information system that integrates a controlled vocabulary, a patient database, and a knowledge base is described. Methods, design and components for the implementation of the system are discussed. It is argued that the key issue for the successful introduction of computer-based decision support in primary care today is integration with a computer-based patient record. Also important is that the knowledge acquisition process is based on the general practitioner's real needs. This has been achieved by, first, providing general practitioners with real patient data from a series of retrospective database studies; and second, letting a panel of general practitioners select, discuss and decide which computer reminders to implement. A hybrid representation scheme was chosen for the knowledge base. The combination of a standard procedural representation (the so-called Arden syntax) for the reminder knowledge with a semantic net representation for the medical factual knowledge facilitates knowledge sharing with other systems and knowledge reuse within the system.

Artificial Intelligence↗

PubMed2006

The tissue microarray object model: a data model for storage, analysis, and exchange of tissue microarray experimental data.

CONTEXT: Tissue microarray (TMA) is an array-based technology allowing the examination of hundreds of tissue samples on a single slide. To handle, exchange, and disseminate TMA data, we need standard representations of the methods used, of the data generated, and of the clinical and histopathologic information related to TMA data analysis. OBJECTIVE: To create a comprehensive data model with flexibility that supports diverse experimental designs and with expressivity and extensibility that enables an adequate and comprehensive description of new clinical and histopathologic data elements. DESIGN: We designed a tissue microarray object model (TMA-OM). Both the array information and the experimental procedure models are created by referring to the microarray gene expression object model, minimum information specification for in situ hybridization and immunohistochemistry experiments, and the TMA data exchange specifications. The clinical and histopathologic information model is created by using College of American Pathologists cancer protocols and National Cancer Institute common data elements. Microarray Gene Expression Data Ontology, the Unified Medical Language System, and the terms extracted from College of American Pathologists cancer protocols and NCI common data elements are used to create a controlled vocabulary for unambiguous annotation. RESULT: The TMA-OM consists of 111 classes in 17 packages to represent clinical and histopathologic information as well as experimental data for any type of cancer. We implemented a Web-based application for TMA-OM, supporting data export in XML format conforming to the TMA data exchange specifications or the document type definition derived from TMA-OM. CONCLUSIONS: The TMA-OM provides a comprehensive data model for storage, analysis, and exchange of TMA data and facilitates model-level integration of other biological models.

Gene Expression Profiling↗

PubMed2006

Cruella: developing a scalable tissue microarray data management system.

CONTEXT: Compared with DNA microarray technology, relatively little information is available concerning the special requirements, design influences, and implementation strategies of data systems for tissue microarray technology. These issues include the requirement to accommodate new and different data elements for each new project as well as the need to interact with pre-existing models for clinical, biological, and specimen-related data. OBJECTIVE: To design and implement a flexible, scalable tissue microarray data storage and management system that could accommodate information regarding different disease types and different clinical investigators, and different clinical investigation questions, all of which could potentially contribute unforeseen data types that require dynamic integration with existing data. DESIGN: The unpredictability of the data elements combined with the novelty of automated analysis algorithms and controlled vocabulary standards in this area require flexible designs and practical decisions. Our design includes a custom Java-based persistence layer to mediate and facilitate interaction with an object-relational database model and a novel database schema. User interaction is provided through a Java Servlet-based Web interface. RESULTS: Cruella has become an indispensable resource and is used by dozens of researchers every day. The system stores millions of experimental values covering more than 300 biological markers and more than 30 disease types. The experimental data are merged with clinical data that has been aggregated from multiple sources and is available to the researchers for management, analysis, and export. CONCLUSION: Cruella addresses many of the special considerations for managing tissue microarray experimental data and the associated clinical information. A metadata-driven approach provides a practical solution to many of the unique issues inherent in tissue microarray research, and allows relatively straightforward interoperability with and accommodation of new data models.

Database Management Systems↗

PubMed1993

Theory and analysis of typical errors in a medical setting.

Medical error is not a medical but rather a behavioral matter. In this article, the differences between errors and accidents are discussed in a theoretical manner. A discussion of some useful taxonomies is followed by suggestions on how to talk about an error. In brief, one must distinguish between the "mode" of a particular error (whether, for example, it is an omission or a substitution), the "expression" of the error (what is actually done in the work environment), and the "consequence" of the error (accident or discovery). Finally, there are suggestions about what can be done about errors and accidents. Some remedies are behavioral and depend on training in self-detection of error. Others depend on changes in the engineering and design of medical systems. The use of controlled vocabulary in prescription writing and in the communication of medical orders is strongly recommended.

Accidents↗

PubMed1998

The development and role of the Read Codes.

Produced and maintained by Britain's National Health Service, the Read Codes are a comprehensive, controlled clinical vocabulary. Here's a look at how the codes evolved, their use in the NHS, and the continual process of aligning the system with the needs of its users.

Abstracting and Indexing↗

PubMed1997

Galen-In-Use: an EU Project applied to the development of a new national coding system for surgical procedures: NCAM.

GALEN has developed a language independent common reference model based on a medically oriented ontology and practical tools and techniques for managing healthcare terminology including natural language processing. GALEN-IN-USE is the current phase which applied the modelling and the tools to the development or the updating of coding systems for surgical procedures in different national coding centre co-operating within the European Federation of Coding Centre (EFCC) to create a multilingual knowledge repository for multicultural Europe. NCAM (Nomenclature Commune des Actes Médicaux) is the new French multipurpose coding system for surgical procedures. The labels are processed from the intermediate dissections to the Grail representation and the natural language generation by the electronically related Medical Informatics research centres network of Saint Etienne, Manchester, Geneva and Nijmegen. The national coding centre is able to retrieve the initial labels with different categories of concepts, to compare the professional language proposed by expert clinicians to the French generated controlled vocabulary and to finalize the linguistic labels of the coding system in relation with the meanings of the conceptual system structure.

Electronic Data Processing↗

PubMed2001

Empirical formulation of a generic query set for clinical information retrieval systems.

Information needs in clinical practice take the form of specific questions about a given clinical situation, and are best satisfied by concise and specific information retrieval. We sought to develop a comprehensive set of generic queries for information retrieval from electronic medical information resources. We collected one hundred and ten real-world questions asked at the point of care in a variety of settings, and from these developed a set of generic queries of which each of the real-world queries could be shown to be a special case. To provide allowed values for each of the concept terms in the queries, we defined generic nouns as unions of UMLS semantic types, and specified which of these were appropriate to each query. We have begun to use the set to index reference texts from general and subspecialty medicine, and found it capable of full text indexing in the clinical domain. We hypothesize that the query set can serve as a basis for more specialized query sets, and that it will remain generalizable to other electronic medical resources, indexing tasks, and non-UMLS controlled vocabularies.

Abstracting and Indexing↗

PubMed2002

On ontologies for biologists: the Gene Ontology--untangling the web.

The mantra of the 'post-genomic' era is 'gene function'. Yet surprisingly little attention has been given to how functional and other information concerning genes is to be captured, made accessible to biologists or structured in a computable form. The aim of the Gene Ontology (GO) Consortium is to provide a framework for both the description and the organisation of such information. The GO Consortium is presently concerned with three structured controlled vocabularies which can be used to describe three discrete biological domains, building structured vocabularies which can be used to describe the molecular function, biological roles and cellular locations of gene products.

Animals↗

PubMed2002

[Transcriptomes for serial analysis of gene expression].

The availability of the sequences for whole genomes is changing our understanding of cell biology. Functional genomics refers to the comprehensive analysis, at the protein level (proteome) and at the mRNA level (transcriptome) of all events associated with the expression of whole sets of genes. New methods have been developed for transcriptome analysis. Serial Analysis of Gene Expression (SAGE) is based on the massive sequential analysis of short cDNA sequence tags. Each tag is derived from a defined position within a transcript. Its size (14 bp) is sufficient to identify the corresponding gene and the number of times each tag is observed provides an accurate measurement of its expression level. Since tag populations can be widely amplified without altering their relative proportions, SAGE may be performed with minute amounts of biological extract. Dealing with the mass of data generated by SAGE necessitates computer analysis. A software is required to automatically detect and count tags from sequence files. Criterias allowing to assess the quality of experimental data can be included at this stage. To identify the corresponding genes, a database is created registering all virtual tags susceptible to be observed, based on the present status of the genome knowledge. By using currently available database functions, it is easy to match experimental and virtual tags, thus generating a new database registering identified tags, together with their expression levels. As an open system, SAGE is able to reveal new, yet unknown, transcripts. Their identification will become increasingly easier with the progress of genome annotation. However, their direct characterization can be attempted, since tag information may be sufficient to design primers allowing to extend unknown sequences. A major advantage of SAGE is that, by measuring expression levels without reference to an arbitrary standard, data are definitively acquired and cumulative. All publicly available data can thus be stored in a unique database, facilitating whole-genome analysis of differential expression between cell types, normal and diseased samples, or samples with and without drug treatment. SAGE data are readily amenable to statistical comparisons, allowing to determine the level of confidence of the observed variations. A major limitation of SAGE is that, because each analysis is obligatory performed on the whole set of expressed genes, it can hardly be performed on multiple samples, for example in kinetics studies or to compare the effects of large numbers of drugs. To overcome this limitation, high-throughput detection of a subset of mRNAs is more rapidly performed by parallel hybridization of mRNAs on arrays of nucleic acids immobilized on solid supports. From this point of view, a SAGE platform is a powerful instrument for selecting the most informative subset of genes, assembling them to design microarrays dedicated to a specific problem and calibrating measurement by comparison with a standard cell model for which SAGE data are available. This approach is an attractive alternative to strategies based exclusively on pangenomic arrays. A very large amount of SAGE data are already available and the problem is now to extract their biological meaning. Knowledge on metabolic pathways is already organized so that its successful integration in a SAGE platform can be undertaken. For other cell components and pathways, the problem lies on the lack of controlled vocabulary to describe gene activities, starting form a clear definition of the concept of biological function itself. Progress in gene and cell ontology is expected to facilitate computer-based extraction of biological knowledge from existing and forthcoming SAGE data.

Animals↗

PubMed2002

Automatic ontology construction from the literature.

Detailed classifications, controlled vocabularies and organised terminology are widely used in different areas of science and technology. Their relatively recent introduction in molecular biology has been crucial for progress in the analysis of genonics and massive proteomics experiments. Unfortunately the construction of the ontologies, including terminology, classification and entity relations requires considerable effort, including the analysis of massive amounts of literature. We propose here a method that automatically generates classifications of gene-product functions using bibliographic information. The corresponding classification structures mirror the ones constructed by human experts. The analysis of a large structure built for yeast gene-products, and the detailed inspection of various examples, show encouraging properties. In particular, the comparison with the well accepted GO ontology points to different situations in which the automatically derived classification can be useful for assisting human experts in the annotation of ontologies.

Computational Biology↗

PubMed1992

Words, concepts, or both: optimal indexing units for automated information retrieval.

What is the best way to represent the content of documents in an information retrieval system? This study compares the retrieval effectiveness of five different methods for automated (machine-assigned) indexing using three test collections. The consistently best methods are those that use indexing based on the words that occur in the available text of each document. Methods used to map text into concepts from a controlled vocabulary showed no advantage over the word-based methods. This study also looked at an approach to relevance feedback which showed benefit for both word-based and concept-based methods.

Abstracting and Indexing↗

PubMed2005

Understanding the foundation: the state of generalist search education in library schools as related to the needs of expert searchers in medical libraries.

PURPOSE: The paper explores the current state of generalist search education in library schools and considers that foundation in respect to the Medical Library Association's statement on expert searching. SETTING/SUBJECTS: Syllabi from courses with significant searching components were examined from ten of the top library schools, as determined by the U.S. News & World Report rankings. METHODOLOGY: Mixed methods were used, but primarily quantitative bibliometric methods were used. RESULTS: The educational focus in these searching components was on understanding the generalist searching resources and typical users and on performing a reflective search through application of search strategies, controlled vocabulary, and logic appropriate to the search tool. There is a growing emphasis on Web-based search tools and a movement away from traditional set-based searching and toward free-text search strategies. While a core set of authors is used in these courses, no core set of readings is used. DISCUSSION/CONCLUSION: While library schools provide a strong foundation, future medical librarians still need to take courses that introduce them to the resources, settings, and users associated with medical libraries. In addition, as more emphasis is placed on Web-based search tools and free-text searching, instructors of the specialist medical informatics courses will need to focus on teaching traditional search methods appropriate for common tools in the medical domain.

Curriculum↗

PubMed1992

Behavior and parental expectations of child pedestrians.

Pedestrian injuries remain the most common cause of death from trauma for young school-age children. This study was based on the hypothesis that parents' abilities to accurately assess their children's street-crossing skills vary with the crossing test and age of the children, being less accurate for younger children. Children at three developmental levels (aged 5 through 6, 7 through 8, and 9 through 10 years) and their parents were evaluated on four street-crossing tests and a control vocabulary test. For each test, children's answers were compared to parents' estimates of their children's performance. Parents overestimated the abilities of their 5-through 6-year-olds on all four tests (P less than .01). Parents overestimated the abilities of 7- through 8-year-olds on two of the tests (P less than .05) and parents accurately assessed the abilities of the 9- through 10-year-olds. On the vocabulary test, parents overestimated their children's performance at all age levels (P less than .01). The results support the hypothesis and indicate that parents' expectations for their children's pedestrian skills are least accurate for 5- and 6-year-olds, with the mismatch decreasing as children get older. Inaccurate expectations of children's pedestrian skills may be fruitful target for injury prevention programs.

Accidents, Traffic↗

PubMed1991

A computerized representation of a medical school curriculum: integration of relational and text management software in database design.

We describe the development of a computer-based representation of the medical school curriculum at the University of North Carolina at Chapel Hill (UNC-CH). Over the past seven years the Medical School's Office of Academic Affairs has employed both relational database and text management software to design an integrated curriculum database system. Depending on the function selected--exploring the curriculum, searching through course outlines, retrieving elective descriptions, identifying teaching faculty, or searching for specific topics--either text management or relational database management routines are activated in a manner transparent to the user. Initial evaluation of the system has been positive but highlights the need for a more robust biomedical language for use as a controlled vocabulary to index content. Efforts are now underway, with support from the Association of American Medical Colleges (AAMC), to engage other interested schools in the U.S. and Canada in collaborating on further development of a system.

Abstracting and Indexing↗