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PubMed2003

Reference terminology for therapeutic goals: a new approach.

Participants at the Nursing Terminology Summit Conference 2002 developed proposed terminology models and information models for findings, goals, and outcomes and described the relationships among these concepts. At Vanderbilt University the author has dissected items in a controlled vocabulary of goals in accordance with the terminology model for findings. The concept-based dissections are linked to pre-coordinated phrases in a relational database, providing unambiguous definitions of clinical expressions and enabling concept-oriented searching of the terms database and of the goal achievement database.

Databases as Topic↗

PubMed2003

Corpus-based associations provide additional morphological variants to medical terminologies.

Knowledge of morphologically derived words, as provided for medical English by the UMLS Specialist Lexicon, is useful to detect term variants for automated coding and indexing. For most other languages though, no comparable morphological knowledge base is available. We therefore endeavored to design general methods to help collect such knowledge for a given language. We propose here a method for discovering derived words in text corpora and apply it to a French medical corpus. To evaluate this method, we study its ability to suggest derived adjectives for 2,297 nouns found in the SNOMED nomenclature, which itself specifies adjectival equivalents for some of its terms. 74% of the proposed adjectives are judged correct (precision) and cover 16% of these nouns (recall), a larger amount than what SNOMED already specifies. Furthermore, the corpus suggests additional adjectives which can increase SNOMED's by 76%. We conclude that such a method can help speed up the construction of a morphological knowledge base which can increase the number of term variants in an existing controlled vocabulary.

Abstracting and Indexing↗

PubMed2004

A knowledge based approach for automated signal generation in pharmacovigilance.

BACKGROUND: Pharmacovigilance experts detect new adverse drug reactions (ADR) by manually reviewing spontaneous reporting systems. Automated signal generation aims to focus the attention of experts on drug-adverse event associations which are disproportionally present in the database. Although adverse events are coded by means of controlled vocabularies such as the MedDRA dictionary, this semantic information is not taken into account for signal generation. OBJECTIVE: To improve the performance of current signal detection algorithms using knowledge based approach. METHOD: We developed a formal ontology of ADRs and built a data mining tool that uses description logic representations of MedDRA terms to group medically related case reports. RESULTS: This knowledge based approach increased the sensitivity of signal detection with no decrease in specificity. DISCUSSION: A knowledge based approach improved the performance of signal detection tools. However, the huge work-load involved in the knowledge engineering step limits the use of this approach for machine learning.

Adverse Drug Reaction Reporting Systems↗

PubMed2005

Non-lexical approaches to identifying associative relations in the gene ontology.

The Gene Ontology (GO) is a controlled vocabulary widely used for the annotation of gene products. GO is organized in three hierarchies for molecular functions, cellular components, and biological processes but no relations are provided among terms across hierarchies. The objective of this study is to investigate three non-lexical approaches to identifying such associative relations in GO and compare them among themselves and to lexical approaches. The three approaches are: computing similarity in a vector space model, statistical analysis of co-occurrence of GO terms in annotation databases, and association rule mining. Five annotation databases (FlyBase, the Human subset of GOA, MGI, SGD, and WormBase) are used in this study. A total of 7,665 associations were identified by at least one of the three non-lexical approaches. Of these, 12% were identified by more than one approach. While there are almost 6,000 lexical relations among GO terms, only 203 associations were identified by both non-lexical and lexical approaches. The associations identified in this study could serve as the starting point for adding associative relations across hierarchies to GO, but would require manual curation. The application to quality assurance of annotation databases is also discussed.

Alzheimer Disease↗

PubMed2005

Grepator: accents & case mix for thesaurus.

There is a real need among researchers and students for pedagogical resources. In France, information retrieval techniques have been developed, for example in the Doc'CISMeF web site. As Pubmed, documents are indexed with (French) MeSH terms, one of the problems discovered, in quality studies, is the inadequacies between the user requests and the MeSH controlled vocabulary. Moreover, French (but also Greek or Spanish), pose specific problems for indexing, due to the diacritic characters.In this article, we present the Grepator project. The main goal is to transform any thesaurus (or any entry) in case mix and accentuated characters, for a specific domain. Furthermore, Grepator has to complete MeSH terms according to their usual form in natural language and finally, to correct user spelling mistakes. Grepator is based on a statistical approach. A large French medical corpus has been constituted from pedagogical resources indexed in CISMeF. Using regular expressions, Grepator searches the more usual ways to spell the word.. Seventy five percent of MeSH terms are found in the corpus, using this method, with less than one mistake for a hundred words. This first evaluation of the tools is analyzed and we discuss further steps that might be developed.

Diagnosis-Related Groups↗

PubMed2006

Landscape of current toxicity databases and database standards.

Having readily available historical information for modeling toxicity has become important throughout the various stages of research and development. The high cost of late-phase attrition and recent international regulatory legislations have made even more acute the need to be able to mine the fragmented data and information available across diverse databases. In addition, the general trend to accelerate regulatory processes globally makes the effective use of existing data an imperative. To achieve efficient screening, develop profiles and gain the ability to cross reference, databases must be interoperated to allow data exchange and integration. Several database standards and controlled vocabulary initiatives have been used in the development of toxicity data models to transform the current landscape. This review describes the major databases of toxicological information now available, and provides a simple example of standardization that demonstrates the benefits of a toxicity database containing such qualified data.

Animals↗

PubMed2005

Creating a vaccine adverse event ontology for public health.

Creation of an ontology for Adverse Events Following Immunization (AEFI) can help immunization stakeholders better understand adverse events. Moreover, it enables the sharing and reuse of machine-readable knowledge and enhances surveillance systems. Linkage of this knowledge base to controlled vocabularies establishes the foundation for integration of information from different applications. This work describes the framework for ontology creation, the collaborative work supporting it and the overall mission of informatics to bridge organizations and enable knowledge sharing.

Adverse Drug Reaction Reporting Systems↗

PubMed1995

Query by pictionary: an alternative to medical image retrieval.

This paper focuses on the visual interface for image retrieval from radiology image database and describes a Radiologic Pictionary. A Radiologic Pictionary is a picture-based controlled vocabulary that allows visual query formulation by providing the user with images (samplers) that are linked to the hierarchical index of radiological findings and mapped into image data within the database. Samplers selected during query formulation point to image records that share their characteristics; all matching images are returned to the user.

Abstracting and Indexing↗

PubMed1996

Standardized nursing classification systems: necessary, but not sufficient, for representing what nurses do.

The American Nurses Association Steering Committee on Databases to Support Nursing Practice has recognized three standardized nursing intervention classification systems. Because each of these classifications systems focuses on encoding informational abstractions of nursing actions, rather than providing a controlled vocabulary and compositional grammar from which informational concepts and abstractions can be constructed, the systems are necessary, but not sufficient, for representing what nurses do. In particular, computer based patient record systems focused on process understanding and process improvement will require atomic-level representations of nursing actions suitable for transformation into a variety of information abstractions, including, but not limited to, the abstractions contained in the three existing classification systems.

Nursing↗

PubMed1996

Modeling principles for QMR medical findings.

Structured representation of medical information is essential for ensuring the accuracy and reliability of computerized decision support applications. Such systems require input that is error-free and clinically pertinent. This paper reviews existing medical models, particularly those exploited for natural language understanding, and highlights modeling features important to future indexing of medical texts with controlled vocabularies. A hybrid representation derived from existing frame-based and conceptual-graph-based systems is proposed to represent relevant medical terms as used by experts.

Abstracting and Indexing↗

PubMed1998

Conceptual graph grammar--a simple formalism for sublanguage.

There are a wide variety of computer applications that deal with various aspects of medical language: concept representation, controlled vocabulary, natural language processing, and information retrieval. While technical and theoretical methods appear to differ, all approaches investigate different aspects of the same phenomenon: medical sublanguage. This paper surveys the properties of medical sublanguage from a formal perspective, based on detailed analyses cited in the literature. A review of several computer systems based on sublanguage approaches shows some of the difficulties in addressing the interaction between the syntactic and semantic aspects of sublanguage. A formalism called Conceptual Graph Grammar is presented that attempts to combine both syntax and semantics into a single notation by extending standard Conceptual Graph notation. Examples from the domain of pathology diagnoses are provided to illustrate the use of this formalism in medical language analysis. The strengths and weaknesses of the approach are then considered. Conceptual Graph Grammar is an attempt to synthesize the common properties of different approaches to sublanguage into a single formalism, and to begin to define a common foundation for language-related research in medical informatics.

Computer Graphics↗

PubMed1998

Clinical application of the UMLS in a computerized order entry and decision-support system.

Vanderbilt University Medical Center uses the UMLS as a dictionary, an interlingua, and a knowledge source within the WizOrder system. WizOrder provides direct care-provider order entry and integrated clinical decision-support capabilities. Linking the two functions enables efficient decision-support during the "normal" workflow of care providers, at the point where decisions are made. WizOrder uses the UMLS as a dictionary to encode free-text entries into controlled vocabularies such as ICD9. As an interlingua, the UMLS provides mapping between vocabularies, allowing to translate patient-specific information to MeSH terms and perform automated literature retrieval. WizOrder uses the tables of co-occurring concepts and the Semantic Network to provide sensible lists of potential drug interactions and adverse drug reactions, and generate fully-formed MEDLINE queries for PubMed.

Decision Support Systems, Clinical↗

PubMedDate not supplied

Development of a computerized database for evaluation of nurse practitioner student clinical experiences in primary health care. Report of three pilot studies.

A computerized database for the collection of patient encounter information by nurse practitioner students provides insight into the number and diversity of cases seen. Menu-driven data entry and controlled vocabulary in the form of diagnostic clusters provide a mechanism to categorize and analyze the data. Faculty are able to review student clinical experience by quantitative measures such as number of patient encounters, diagnostic cluster of the encounter, and patient demographic data. Qualitative measures such as student's level of responsibility and student prior experience with stated encounter also are included in the database. Data analysis of two interdisciplinary pilots provided comparisons across disciplines of nursing, medicine, and physical therapy. A subsequent nurse practitioner student pilot provided further refinement and a broadened database terminology more inclusive of a nursing perspective. Educational and clinical issues involved in development, maintenance, and future use of the database are discussed.

Clinical Competence↗

PubMed1994

Designing a controlled medical vocabulary server: the VOSER project.

The authors describe their experience designing a controlled medical vocabulary server created to support the exchange of patient data and medical decision logic. The first section introduces practical and theoretical premises that guided the design of the vocabulary server. The second section describes a series of structures needed to implement the proposed server, emphasizing their conformance to the design premises. The third section introduces potential applications that provide services to end users and also a group of tools necessary for maintaining the server corpus. In the fourth section, the authors propose an implementation strategy based on a common framework and on the participation of groups from different health-related domains.

Clinical Laboratory Information Systems↗

PubMed2004

IntAct: an open source molecular interaction database.

IntAct provides an open source database and toolkit for the storage, presentation and analysis of protein interactions. The web interface provides both textual and graphical representations of protein interactions, and allows exploring interaction networks in the context of the GO annotations of the interacting proteins. A web service allows direct computational access to retrieve interaction networks in XML format. IntAct currently contains approximately 2200 binary and complex interactions imported from the literature and curated in collaboration with the Swiss-Prot team, making intensive use of controlled vocabularies to ensure data consistency. All IntAct software, data and controlled vocabularies are available at http://www.ebi.ac.uk/intact.

Animals↗

PubMed1996

Utilizing OODB schema modeling for vocabulary management.

Comprehension of complex controlled vocabularies is often difficult. We present a method, facilitated by an object-oriented database, for depicting such a vocabulary (the Medical Entities Dictionary (MED) from the Columbia-Presbyterian Medical Center) in a schematic way which uses a sparse inheritance network of area classes. The resulting Object Oriented Health Vocabulary repository (OOHVR) allows visualization of the 43,000 MED concepts as 90 area classes. This view has provided valuable information to those responsible with maintaining the MED. As a result, the MED organization has been improved and some previously-unrecognized errors and inconsistencies have been removed. We believe that this schematic approach allows improved comprehension of the gestalt of large controlled medical vocabulary.

Classification↗

PubMedDate not supplied

A continuous-speech interface to a decision support system: I. Techniques to accommodate for misrecognized input.

OBJECTIVE: Develop a continuous-speech interface that allows flexible input of clinical findings into a medical diagnostic application. DESIGN: The authors' program allows users to enter clinical findings using their own vernacular. It displays from the diagnostic program's controlled vocabulary a list of terms that most closely matches the input, and allows the user to select the single best term. The interface program includes two components: a speech-recognition component that converts utterances into text strings, and a language-processing component that matches recognized text strings with controlled-vocabulary terms. The speech-recognition component is composed of commercially available speech-recognition hardware and software, and developer-created grammars, which specify the language to be recognized. The language-processing component is composed of a translator, which extracts a canonical form from both recognized text strings and controlled-vocabulary terms, and a matcher, which measures the similarity between the two canonical forms. RESULTS: The authors discovered that grammars constructed by a physician, who could anticipate how users might speak findings, supported speech recognition better than did grammars constructed programmatically from the controlled vocabulary. However, this programmatic method of grammar construction was more time efficient and better supported long-term maintenance of the grammars. The authors also found that language-processing techniques recovered some of the information lost due to speech misrecognition, but were dependent on the completeness of supporting synonym dictionaries. CONCLUSIONS: The authors' program demonstrated the feasibility of using continuous speech to enter findings into a medical application. However, improvements in speech-recognition technology and language-processing techniques are needed before natural continuous speech becomes an acceptable input modality for clinical applications.

Diagnosis, Computer-Assisted↗

PubMed2006

Limited search strategies were effective in finding relevant nonrandomized studies.

BACKGROUND AND OBJECTIVE: Searching for nonrandomized studies in electronic databases is complicated because there is a variety of study designs and lack of standardization in the terminology. The purpose of this study was to develop and evaluate a method to limit search strategies according to study design of comparative nonrandomized studies (cNRSs). METHODS: Four updated Cochrane systematic reviews that included nonrandomized studies (cohort, case-control, and cross-sectional studies) of the effects of health care interventions were selected. Search strategies limited to study design were devised for each one of these topic areas in two electronic databases (MEDLINE and EMBASE). A progressive method (PM) and a fixed method for selecting the most appropriate search terms associated with study design of nonrandomized studies are suggested. RESULTS: The results showed that the sensitivity of search strategies (in two databases combined) limited to study design were between 90% and 100% for the PM using both controlled vocabulary (CV) and textwords (TWs) and between 95% and 100% for a fixed set of controlled vocabulary and TWs. CONCLUSIONS: It is possible and acceptable to use search strategies limited to study design of cNRSs of health care interventions.

Databases, Bibliographic↗