Impacto de la Guía Diagnóstica Móvil. Sobre el aprendizaje del lenguaje enfermero normalizado / Impact of the mobile diagnostic guide over the learning of the normalized nursing language

Objetivos. Este trabajo está guiado por dos objetivos: 1) presentar una Guía Diagnóstica Móvil de acceso público y basada en las interrelaciones en taxonomías de lenguaje enfermero normalizado. Esta herramienta se ha liberado para proporcionar apoyo a profeionales de enfermería en el aprendizaje del mismo y en la elaboración de planes de cuidado; 2) evaluar la usabilidad de la aplicación y el impacto de su uso en las variables de motivación intrínseca y aprendizaje. Metodología. Estudio analítico pre y postest en el que se ha utilizado una muestra de 30 enfermeros del ámbito de los cuidados intensivos, siendo la duración del experimento de 7 días por usuario. Resultados. El experimento concluye resultados positivos en la usabilidad de la aplicación y en la motivación intrínseca de los participantes. En cuanto a los conocimientos adquiridos sobre el lenguaje enfermero, se evidenció un incremento en sus conocimientos del pretest al postest. Conclusiones. El uso de tecnología ubicua en un entorno profesional contribuye positivamente a la adquisición de conocimientos y a la motivación en el aprendizaje posgradual del lenguaje normalizado. Los resultados de este experimento sugieren la necesidad en profesionales de enfermería de tener acceso ubicuo a información relevante. El potencial de estas herramientas para la calidad de la práctica asistencial es mucho mayor cuando se realiza desde dispositivos móviles personales (AU)

Objectives. This research has been guided by two main objectives: ) presenting an open mobile diagnosing tool designed based on the interrelationships existing within the normalized nursing language to provide learning support to nursing professionals in the elaboration of attention plans; 2) presenting the results from an experiment evaluating the impact of mobile learning support in the variables of intrinsic motivation and knowledge. Method. A pre- and postevaluation experiment was performed on 30 nursing professionals for 7 days. Results. As an outcome of using the mobile diagnosing tool in a professional context, the reports conclude positive means in the usability and the intrinsic motivation from the participants. Moreover, the knowledge of the normalized nursing language increased from the pre to the post evaluation. Conclusions. The results from this experiment rekindle the need of learning support from nursing professionals in situ and in time. Mobile devices have the potential to provide ubiquitous access to content in context. These request are even more powerful when they are accomplished from personal devices (AU)

Utilización adecuada del lenguaje médico: principales problemas y soluciones / Proper use of medical language: Main problems and solutions

El lenguaje médico debe caracterizarse por su precisión, neutralidad emocional y estabilidad. La comunicación efectiva de los resultados de los trabajos científicos depende del cumplimiento de las normas vigentes de redacción y estilo, de manera que en un texto con defectos formales puede quedar mermado el interés de los hallazgos. En este trabajo se comentan algunos de los problemas y defectos más habituales del lenguaje médico; entre ellos, el abuso de siglas y de extranjerismos, la utilización de palabras impropias, los errores sintácticos o solecismos, los defectos más frecuentes en los títulos, y el abuso de mayúsculas y del gerundio. Frente a estos problemas, el investigador dispone de armas eficaces para superarlos, como la consulta de textos de calidad, los diccionarios críticos de dudas y dificultades de la lengua española y los manuales de redacción y estilo (AU)

Medical language should be characterized by its precision, emotional neutrality and stability. The effective communication of results of scientific studies depends on compliance with current standards of drafting and style; texts with defects can hinder interest in the findings. In this study, we discuss some of the most common problems and errors in medical language, including the abuse of abbreviations and foreign words, the use of improper words, syntax errors and solecisms, the most common errors in titles and the abuse of capital letters and the gerund. Investigators have effective tools for dealing with these problems, such as quality texts, critical dictionaries of questions and difficulties with the Spanish language and various drafting and style manuals (AU)

Comparison of UMLS terminologies to identify risk of heart disease using clinical notes.

The second track of the 2014 i2b2 challenge asked participants to automatically identify risk factors for heart disease among diabetic patients using natural language processing techniques for clinical notes. This paper describes a rule-based system developed using a combination of regular expressions, concepts from the Unified Medical Language System (UMLS), and freely-available resources from the community. With a performance (F1=90.7) that is significantly higher than the median (F1=87.20) and close to the top performing system (F1=92.8), it was the best rule-based system of all the submissions in the challenge. We also used this system to evaluate the utility of different terminologies in the UMLS towards the challenge task. Of the 155 terminologies in the UMLS, 129 (76.78%) have no representation in the corpus. The Consumer Health Vocabulary had very good coverage of relevant concepts and was the most useful terminology for the challenge task. While segmenting notes into sections and lists has a significant impact on the performance, identifying negations and experiencer of the medical event results in negligible gain.

COHeRE: Cross-Ontology Hierarchical Relation Examination for Ontology Quality Assurance.

Biomedical ontologies play a vital role in healthcare information management, data integration, and decision support. Ontology quality assurance (OQA) is an indispensable part of the ontology engineering cycle. Most existing OQA methods are based on the knowledge provided within the targeted ontology. This paper proposes a novel cross-ontology analysis method, Cross-Ontology Hierarchical Relation Examination (COHeRE), to detect inconsistencies and possible errors in hierarchical relations across multiple ontologies. COHeRE leverages the Unified Medical Language System (UMLS) knowledge source and the MapReduce cloud computing technique for systematic, large-scale ontology quality assurance work. COHeRE consists of three main steps with the UMLS concepts and relations as the input. First, the relations claimed in source vocabularies are filtered and aggregated for each pair of concepts. Second, inconsistent relations are detected if a concept pair is related by different types of relations in different source vocabularies. Finally, the uncovered inconsistent relations are voted according to their number of occurrences across different source vocabularies. The voting result together with the inconsistent relations serve as the output of COHeRE for possible ontological change. The highest votes provide initial suggestion on how such inconsistencies might be fixed. In UMLS, 138,987 concept pairs were found to have inconsistent relationships across multiple source vocabularies. 40 inconsistent concept pairs involving hierarchical relationships were randomly selected and manually reviewed by a human expert. 95.8% of the inconsistent relations involved in these concept pairs indeed exist in their source vocabularies rather than being introduced by mistake in the UMLS integration process. 73.7% of the concept pairs with suggested relationship were agreed by the human expert. The effectiveness of COHeRE indicates that UMLS provides a promising environment to enhance qualities of biomedical ontologies by performing cross-ontology examination.

Auditing hierarchical cycles to locate other inconsistencies in the UMLS.

A cycle in the parent relationship hierarchy of the UMLS is a configuration that effectively makes some concept(s) an ancestor of itself. Such a structural inconsistency can easily be found automatically. A previous strategy for disconnecting cycles is to break them with the deletion of one or more parent relationships-irrespective of the correctness of the deleted relationships. A methodology is introduced for auditing of cycles that seeks to discover and delete erroneous relationships only. Cycles involving three concepts are the primary consideration. Hypotheses about the high probability of locating an erroneous parent relationship in a cycle are proposed and confirmed with statistical confidence and lend credence to the auditing approach. A cycle may serve as an indicator of other non-structural inconsistencies that are otherwise difficult to detect automatically. An extensive auditing example shows how a cycle can indicate further inconsistencies.

Using Unified Modelling Language (UML) as a process-modelling technique for clinical-research process improvement.

The Primary Care Data Quality programme (PCDQ) is a quality-improvement programme which processes routinely collected general practice computer data. Patient data collected from a wide range of different brands of clinical computer systems are aggregated, processed, and fed back to practices in an educational context to improve the quality of care. Process modelling is a well-established approach used to gain understanding and systematic appraisal, and identify areas of improvement of a business process. Unified modelling language (UML) is a general purpose modelling technique used for this purpose. We used UML to appraise the PCDQ process to see if the efficiency and predictability of the process could be improved. Activity analysis and thinking-aloud sessions were used to collect data to generate UML diagrams. The UML model highlighted the sequential nature of the current process as a barrier for efficiency gains. It also identified the uneven distribution of process controls, lack of symmetric communication channels, critical dependencies among processing stages, and failure to implement all the lessons learned in the piloting phase. It also suggested that improved structured reporting at each stage - especially from the pilot phase, parallel processing of data and correctly positioned process controls - should improve the efficiency and predictability of research projects. Process modelling provided a rational basis for the critical appraisal of a clinical data processing system; its potential maybe underutilized within health care.

An expert study evaluating the UMLS lexical metaschema.

OBJECTIVE: A metaschema is an abstraction network of the UMLS's semantic network (SN) obtained from a connected partition of its collection of semantic types. A lexical metaschema was previously derived based on a lexical partition which partitioned the SN into semantic-type groups using identical word-usage among the names of semantic types and the definitions of their respective children. In this paper, a statistical analysis methodology is presented to evaluate the lexical metaschema based on a study involving a group of established UMLS experts. METHODS: In the study, each expert was asked to identify subject areas of the SN based on his or her understanding of the various semantic types. For this purpose, the expert scans the SN hierarchy top-down, identifying semantic types, which are important and different enough from their parent semantic types, as roots of their groups. From the response of each expert, an "expert metaschema" is constructed. The different experts' metaschemas can vary widely. So, additional metaschemas are obtained from aggregations of the experts' responses. Of special interest is the consensus metaschema which represents an aggregation of a simple majority of the experts' responses. Statistical analysis comparing the lexical metaschema with the experts' metaschemas and the consensus metaschema is presented. RESULTS: The analysis results shows that 17 out of the 21 meta-semantic types in the lexical metaschema also appear in the consensus metaschema (about 81%). There are 107 semantic types (about 79%) covered by identical meta-semantic types and refinements. The results show the high similarity between the two metaschemas. Furthermore, the statistical analysis shows that the lexical metaschema did not grossly underperform compared to the experts. CONCLUSION: Our study shows that the lexical metaschema provides a good approximation for a partition of meaningful subject areas in the SN, when compared to the consensus metaschema capturing the aggregation of a simple majority of the human experts' opinions.

Integrating SNOMED CT into the UMLS: an exploration of different views of synonymy and quality of editing.

OBJECTIVE: The integration of SNOMED CT into the Unified Medical Language System (UMLS) involved the alignment of two views of synonymy that were different because the two vocabulary systems have different intended purposes and editing principles. The UMLS is organized according to one view of synonymy, but its structure also represents all the individual views of synonymy present in its source vocabularies. Despite progress in knowledge-based automation of development and maintenance of vocabularies, manual curation is still the main method of determining synonymy. The aim of this study was to investigate the quality of human judgment of synonymy. DESIGN: Sixty pairs of potentially controversial SNOMED CT synonyms were reviewed by 11 domain vocabulary experts (six UMLS editors and five noneditors), and scores were assigned according to the degree of synonymy. MEASUREMENTS: The synonymy scores of each subject were compared to the gold standard (the overall mean synonymy score of all subjects) to assess accuracy. Agreement between UMLS editors and noneditors was measured by comparing the mean synonymy scores of editors to noneditors. RESULTS: Average accuracy was 71% for UMLS editors and 75% for noneditors (difference not statistically significant). Mean scores of editors and noneditors showed significant positive correlation (Spearman's rank correlation coefficient 0.654, two-tailed p < 0.01) with a concurrence rate of 75% and an interrater agreement kappa of 0.43. CONCLUSION: The accuracy in the judgment of synonymy was comparable for UMLS editors and nonediting domain experts. There was reasonable agreement between the two groups.

An enriched unified medical language system semantic network with a multiple subsumption hierarchy.

OBJECTIVE: The Unified Medical Language System's (UMLS's) Semantic Network's (SN's) two-tree structure is restrictive because it does not allow a semantic type to be a specialization of several other semantic types. In this article, the SN is expanded into a multiple subsumption structure with a directed acyclic graph (DAG) IS-A hierarchy, allowing a semantic type to have multiple parents. New viable IS-A links are added as warranted. DESIGN: Two methodologies are presented to identify and add new viable IS-A links. The first methodology is based on imposing the characteristic of connectivity on a previously presented partition of the SN. Four transformations are provided to find viable IS-A links in the process of converting the partition's disconnected groups into connected ones. The second methodology identifies new IS-A links through a string matching process involving names and definitions of various semantic types in the SN. A domain expert is needed to review all the results to determine the validity of the new IS-A links. RESULTS: Nineteen new IS-A links are added to the SN, and four new semantic types are also created to support the multiple subsumption framework. The resulting network, called the Enriched Semantic Network (ESN), exhibits a DAG-structured hierarchy. A partition of the ESN containing 19 connected groups is also derived. CONCLUSION: The ESN is an expanded abstraction of the UMLS compared with the original SN. Its multiple subsumption hierarchy can accommodate semantic types with multiple parents. Its representation thus provides direct access to a broader range of subsumption knowledge.

Why an ICNP? Links among quality, information and policy.

Work has been underway for over a decade to develop and use the International Classification for Nursing Practice (ICNP). The International Council of Nurses has released ICNP Alpha and Beta versions. This article is an adaptation of the presentation given at the International Council of Nurses Congress on June 12th, 2001, in Copenhagen, Denmark. When the ICNP was originally drafted, many uses were envisioned. Key among these were that the information generated would be used by clinicians, researchers, administrators and policy makers. This article introduces examples of these uses.

Enriching the structure of the UMLS semantic network.

The Unified Medical Language System's (UMLS's) Semantic Network (SN)---consisting of a network of semantic types---has a two-tree structure, where each semantic type has at most one parent semantic type. This arrangement is restrictive because some semantic types are, by their definition, specializations of several parents. As a proposed enhancement to the SN, its semantic types have previously been partitioned into groups, each of which contains semantic types of some specific area. However, some groups of this proposed partition contain forest (i.e., multiple-tree) structures or even isolated semantic types. Both situations imply a disconnected internal structure. Connectivity is actually one way to assess the proposed "semantic validity" principle for partitions. It is a desired, although not required, property. In this paper, we introduce a methodology for identifying "missing" IS-A links and adding them to the SN. This process transforms the SN into a Directed Acyclic Graph (DAG) structure, with semantic types permitted to have multiple parents. A result of our methodology is the transformation of the proposed SN partition into groups satisfying the connectivity property.

Editorial 317 Prog Obstet Ginecol 2002;45(8):317-8 En este número de P ROGRESOS DE O BSTETRICIA Y G INECOLOGÍA se publica el informe del Comité a d hoc constituido por miembros de la Sección de Medicina Perinatal de la Sociedad Española de Obstetricia y Ginecología ( SEGO) y de la Sociedad Española de Neonatología (SEN) / No disponible

No disponible

Evaluation and application of a semantic network partition.

Semantic networks (SNs) are excellent knowledge representation structures. However, large semantic networks are hard to comprehend. To overcome this difficulty, several methods of partitioning have been developed that rely on different mixes of structural and semantic methods. However, little has appeared in the literature concerning the question whether a partition of a semantic network creates subnetworks that agree with human insight. We address this issue by presenting a comparison between the results of an algorithmic partitioning method and a partition created by a group of experts. Subsequently, we show how a network partition can be used to generate various partial views of a semantic network, which facilitate user orientation. Examples from the Unified Medical Language System (UMLS) SN are used to demonstrate partial views.

Partitioning the UMLS semantic network.

The unified medical language system (UMLS) integrates many well-established biomedical terminologies. The UMLS semantic network (SN) can help orient users to the vast knowledge content of the UMLS Metathesaurus (META) via its abstract conceptual view. However, the SN itself is large and complex and may still be difficult to comprehend. Our technique partitions the SN into smaller meaningful units amenable to display on limited-sized computer screens. The basis for the partitioning is the distribution of the relationships within the SN. Three rules are applied to transform the original partition into a second more cohesive partition.

Dental concepts in the Unified Medical Language System.

OBJECTIVE: Currently, no comprehensive, controlled vocabulary for dentistry is available. The objective of this study was to determine how well the Unified Medical Language System, the largest repository of concepts and terms in biomedicine, represents dental concepts. METHOD AND MATERIALS: The dental subset of concepts was extracted from Unified Medical Language System using the software program APEX (APplication for the EXtraction of domain-specific concepts). The relationships contained in the Unified Medical Language System Metathesaurus were used to locate the concepts related to 12 seed terms. The Encyclopédie Médico-Chirurgicale (513 dental terms) and the Diagnostic Codes developed by Leake et al (124 terms) were compared to the dental subset. Terms were classified as exact match, related term, or no match. The resultant matching characteristics were compared to those determined by the National Library of Medicine/Agency for Health Care Policy and Research Large Scale Vocabulary Test. RESULTS: The dental subset of Unified Medical Language System contained 948 concepts. The Encyclopédie Médico-Chirurgicale and the Diagnostic Codes exhibited similar matching characteristics for exact match (61.6% and 58.9%, respectively) and related term (38.0% and 32.2%, respectively). For no match, the matching frequencies were significantly different (P < .001). CONCLUSION: The Unified Medical Language System may be a comprehensive source of terms suitable for various representation requirements in dentistry.

An object-oriented model for representing semantic locality in the UMLS.

Several information models have been developed for the Unified Medical Language System (UMLS). While some models are term-oriented, a knowledge-oriented model is needed for representing semantic locality, i.e. the various semantic links among concepts. We propose an object-oriented model in which the semantic features of the UMLS are made available through four major classes for representing Metathesaurus concepts, semantic types, inter-concept relationships and Semantic Network relationships. Additional semantic methods for reducing the complexity of the hierarchical relationships represented in the UMLS are proposed. Implementation details are presented, as well as examples of use. The interest of this approach is discussed.

Methods for exploring the semantics of the relationships between co-occurring UMLS concepts.

OBJECTIVE: To characterize the relationships among UMLS concepts that co-occur as MeSH descriptors in MEDLINE citations (1990-1999). DESIGN: 18,485 UMLS concepts involved in 7,928,608 directed pairs of co-occurring concepts were studied. For each directed pair of concepts C1-C2: (i) the "family" of C1 was built, using the UMLS Metathesaurus, and we tested whether or not C2 belonged to C1's family; (ii) we used the semantic categorization of Metathesaurus concepts through the UMLS Semantic Network and Semantic Groups to represent the semantics of the relationships between C1 and C2. RESULTS: In 6.5% of the directed pairs, the co-occurring concept C2 was found within the "family" of C1. Detailed results are given. The most frequent co-occurrences involved "Chemicals and Drugs" and "Chemicals and Drugs", as well as "Disorders" and "Chemicals and Drugs". DISCUSSION: This work takes advantage of both symbolic and statistical information represented in the UMLS, and analyzes their overlap. Further research is suggested.

Aggregating UMLS semantic types for reducing conceptual complexity.

The conceptual complexity of a domain can make it difficult for users of information systems to comprehend and interact with the knowledge embedded in those systems. The Unified Medical Language System (UMLS) currently integrates over 730,000 biomedical concepts from more than fifty biomedical vocabularies. The UMLS semantic network reduces the complexity of this construct by grouping concepts according to the semantic types that have been assigned to them. For certain purposes, however, an even smaller and coarser-grained set of semantic type groupings may be desirable. In this paper, we discuss our approach to creating such a set. We present six basic principles, and then apply those principles in aggregating the existing 134 semantic types into a set of 15 groupings. We present some of the difficulties we encountered and the consequences of the decisions we have made. We discuss some possible uses of the semantic groups, and we conclude with implications for future work.

Circular hierarchical relationships in the UMLS: etiology, diagnosis, treatment, complications and prevention.

The Unified Medical Language System (UMLS) is a large repository of some 800,000 concepts for the biomedical domain, organized by several millions of inter-concept relationships, either inherited from the source vocabularies, or specifically generated. This paper focuses on hierarchical relationships in the UMLS Metathesaurus, and especially, on circular hierarchical relationships. Using the metaphor of a disease, we first analyze the causal mechanisms for circular hierarchical relationships. Then, we discuss methods to identify and remove these relationships. Finally, we briefly discuss the consequences of these relationships for applications based on the UMLS, and we propose some prevention measures.