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1.
Int J Med Inform ; 129: 100-106, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31445243

RESUMO

BACKGROUND: This work deals with Natural Language Processing applied to the clinical domain. Specifically, the work deals with a Medical Entity Recognition (MER) on Electronic Health Records (EHRs). Developing a MER system entailed heavy data preprocessing and feature engineering until Deep Neural Networks (DNNs) emerged. However, the quality of the word representations in terms of embedded layers is still an important issue for the inference of the DNNs. GOAL: The main goal of this work is to develop a robust MER system adapting general-purpose DNNs to cope with the high lexical variability shown in EHRs. In addition, given that EHRs tend to be scarce when there are out-domain corpora available, the aim is to assess the impact of the word representations on the performance of the MER as we move to other domains. In this line, exhaustive experimentation varying information generation methods and network parameters are crucial. METHODS: We adapted a general purpose sequential tagger based on Bidirectional Long-Short Term Memory cells and Conditional Random Fields (CRFs) in order to make it tolerant to high lexical variability and a limited amount of corpora. To this end, we incorporated part of speech (POS) and semantic-tag embedding layers to the word representations. RESULTS: One of the strengths of this work is the exhaustive evaluation of dense word representations obtained varying not only the domain and genre but also the learning algorithms and their parameter settings. With the proposed method, we attained an error reduction of 1.71 (5.7%) compared to the state-of-the-art even that no preprocessing or feature engineering was used. CONCLUSIONS: Our results indicate that dense representations built taking word order into account leverage the entity extraction system. Besides, we found that using a medical corpus (not necessarily EHRs) to infer the representations improves the performance, even if it does not correspond to the same genre.


Assuntos
Processamento de Linguagem Natural , Algoritmos , Registros Eletrônicos de Saúde , Redes Neurais de Computação , Semântica , Descritores
2.
AMIA Annu Symp Proc ; 2019: 1129-1138, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-32308910

RESUMO

With advances in Machine Learning (ML), neural network-based methods, such as Convolutional/Recurrent Neural Networks, have been proposed to assist terminology curators in the development and maintenance of terminologies. Bidirectional Encoder Representations from Transformers (BERT), a new language representation model, obtains state-of-the-art results on a wide array of general English NLP tasks. We explore BERT's applicability to medical terminology-related tasks. Utilizing the "next sentence prediction" capability of BERT, we show that the Fine-tuning strategy of Transfer Learning (TL) from the BERTBASE model can address a challenging problem in automatic terminology enrichment - insertion of new concepts. Adding a pre-training strategy enhances the results. We apply our strategies to the two largest hierarchies of SNOMED CT, with one release as training data and the following release as test data. The performance of the combined two proposed TL models achieves an average F1 score of 0.85 and 0.86 for the two hierarchies, respectively.


Assuntos
Aprendizado de Máquina , Processamento de Linguagem Natural , Descritores , Systematized Nomenclature of Medicine , Redes Neurais de Computação
3.
Syst Rev ; 7(1): 200, 2018 11 20.
Artigo em Inglês | MEDLINE | ID: mdl-30458825

RESUMO

BACKGROUND: Researchers performing systematic reviews (SRs) must carefully consider the relevance of thousands of citations retrieved from bibliographic database searches, the majority of which will be excluded later on close inspection. Well-developed bibliographic searches are generally created with thesaurus or index terms in combination with keywords found in the title and/or abstract fields of citation records. Records in the bibliographic database Embase contain many more thesaurus terms than MEDLINE. Here, we aim to examine how limiting searches to major thesaurus terms (in MEDLINE called focus terms) in Embase and MEDLINE as well as limiting to words in the title and abstract fields of those databases affects the overall recall of SR searches. METHODS: To examine the impact of using search techniques aimed at higher precision, we analyzed previously completed SRs and focused our original searches to major thesaurus terms or terms in title and/or abstract only in Embase.com or in Embase.com and MEDLINE (Ovid) combined. We examined the total number of search results in both Embase and MEDLINE and checked whether included references were retrieved by these more focused approaches. RESULTS: For 73 SRs, we limited Embase searches to major terms only while keeping the search in MEDLINE and other databases such as Web of Science as they were. The overall search yield (or total number of search results) was reduced by 8%. Six reviews (9%) lost more than 5% of the relevant references. Limiting Embase and MEDLINE to major thesaurus terms, the number of references was 13% lower. For 15% of the reviews, the loss of relevant references was more than 5%. Searching Embase for title and abstract caused a loss of more than 5% in 16 reviews (22%), while limiting Embase and MEDLINE that way this happened in 24 reviews (33%). CONCLUSIONS: Of the four search options, two options substantially reduced the overall search yield. However, this also resulted in a greater chance of losing relevant references, even though many references were still found in other databases such as Web of Science.


Assuntos
Bases de Dados Bibliográficas , Armazenamento e Recuperação da Informação/métodos , MEDLINE , Descritores , Humanos , Estudos Prospectivos , Ferramenta de Busca , Revisões Sistemáticas como Assunto
4.
Psychiatr Danub ; 30(3): 317-322, 2018 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-30267524

RESUMO

BACKGROUND: Suicide is a complex action of suicidal methods and peripheral factors with seemingly threatening components representing actual cause for the suicidal actions. It is especially those, apparently unimportant factors that represent a crucial milestone in the network of all the other, personal, cultural, genetic and biochemical factors, forming the method of action consequently deciding between life and death. SUBJECTS AND METHODS: Based on the Register of Suicides in the Republic of Slovenia kept by the University Psychiatric Clinic Ljubljana, we used a combination of attributes varying within a variable and between variables. Due to limited application of standard statistical methods and analyses in such cases, we used the Machine learning method, Multimethod hybrid approach, which allows combining of different approaches to machine learning (decision trees, genetic algorithms and supplementary vectors). The research included 56712 persons attempting suicide and 21913 persons committing suicide. We chose a form of a suicide action with both possible results: attempted suicide and suicide. RESULTS: Based on the analysis of machine learning, we defined attributes of the action regarding their lethal effect: attempted suicide and suicide commitment. The suicide register kept for the last 40 years shows hanging as the most commonly used suicidal method, used by men with the purpose of causing suicidal death rather than a suicidal attempt. On the other hand, use of medicaments is linked to the suicidal attempt and mostly used by females. CONCLUSIONS: All methods of suicidal actions cannot predict suicidal death, thus we examined different methods of suicide to most accurately predict the link between the method and its effect in terms of suicide attempt or suicide. The Machine learning method confirmed the attributes of suicide methods in connection with their different outcomes. This analytical method is useful in processing large databases since it enables one variable's intensity to affect other variables in terms of result and meaning. The identification of the most decisive risk factors for suicidal behaviour can serve as basis for planning an effective prevention strategies, timely identification and adequate proffessional help to the high risk persons.


Assuntos
Descritores , Tentativa de Suicídio/psicologia , Tentativa de Suicídio/estatística & dados numéricos , Suicídio/psicologia , Suicídio/estatística & dados numéricos , Adolescente , Adulto , Causas de Morte , Feminino , Humanos , Aprendizado de Máquina , Masculino , Pessoa de Meia-Idade , Mortalidade , Análise Multivariada , Sistema de Registros/estatística & dados numéricos , Fatores de Risco , Fatores Sexuais , Eslovênia , Ideação Suicida , Suicídio/prevenção & controle , Tentativa de Suicídio/prevenção & controle , Adulto Jovem
5.
Res Synth Methods ; 9(4): 587-601, 2018 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-30103261

RESUMO

OBJECTIVE: Identify the most performant automated text classification method (eg, algorithm) for differentiating empirical studies from nonempirical works in order to facilitate systematic mixed studies reviews. METHODS: The algorithms were trained and validated with 8050 database records, which had previously been manually categorized as empirical or nonempirical. A Boolean mixed filter developed for filtering MEDLINE records (title, abstract, keywords, and full texts) was used as a baseline. The set of features (eg, characteristics from the data) included observable terms and concepts extracted from a metathesaurus. The efficiency of the approaches was measured using sensitivity, precision, specificity, and accuracy. RESULTS: The decision trees algorithm demonstrated the highest performance, surpassing the accuracy of the Boolean mixed filter by 30%. The use of full texts did not result in significant gains compared with title, abstract, keywords, and records. Results also showed that mixing concepts with observable terms can improve the classification. SIGNIFICANCE: Screening of records, identified in bibliographic databases, for relevant studies to include in systematic reviews can be accelerated with automated text classification.


Assuntos
Bases de Dados Bibliográficas , Armazenamento e Recuperação da Informação/métodos , Projetos de Pesquisa , Algoritmos , Teorema de Bayes , Mineração de Dados/métodos , Humanos , Armazenamento e Recuperação da Informação/normas , Modelos Estatísticos , Reconhecimento Automatizado de Padrão , Padrões de Referência , Ferramenta de Busca , Sensibilidade e Especificidade , Descritores , Máquina de Vetores de Suporte , Revisões Sistemáticas como Assunto
6.
Sao Paulo Med J ; 136(2): 103-108, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29340504

RESUMO

BACKGROUND: A high-quality electronic search is essential for ensuring accuracy and comprehensiveness among the records retrieved when conducting systematic reviews. Therefore, we aimed to identify the most efficient method for searching in both MEDLINE (through PubMed) and EMBASE, covering search terms with variant spellings, direct and indirect orders, and associations with MeSH and EMTREE terms (or lack thereof). DESIGN AND SETTING: Experimental study. UNESP, Brazil. METHODS: We selected and analyzed 37 search strategies that had specifically been developed for the field of anesthesiology. These search strategies were adapted in order to cover all potentially relevant search terms, with regard to variant spellings and direct and indirect orders, in the most efficient manner. RESULTS: When the strategies included variant spellings and direct and indirect orders, these adapted versions of the search strategies selected retrieved the same number of search results in MEDLINE (mean of 61.3%) and a higher number in EMBASE (mean of 63.9%) in the sample analyzed. The numbers of results retrieved through the searches analyzed here were not identical with and without associated use of MeSH and EMTREE terms. However, association of these terms from both controlled vocabularies retrieved a larger number of records than did the use of either one of them. CONCLUSIONS: In view of these results, we recommend that the search terms used should include both preferred and non-preferred terms (i.e. variant spellings and direct/indirect order of the same term) and associated MeSH and EMTREE terms, in order to develop highly-sensitive search strategies for systematic reviews.


Assuntos
Anestesiologia , Armazenamento e Recuperação da Informação/métodos , Literatura de Revisão como Assunto , Ferramenta de Busca/métodos , Descritores , Humanos , MEDLINE
7.
Res Synth Methods ; 9(4): 602-614, 2018 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-29314757

RESUMO

Machine learning (ML) algorithms have proven highly accurate for identifying Randomized Controlled Trials (RCTs) but are not used much in practice, in part because the best way to make use of the technology in a typical workflow is unclear. In this work, we evaluate ML models for RCT classification (support vector machines, convolutional neural networks, and ensemble approaches). We trained and optimized support vector machine and convolutional neural network models on the titles and abstracts of the Cochrane Crowd RCT set. We evaluated the models on an external dataset (Clinical Hedges), allowing direct comparison with traditional database search filters. We estimated area under receiver operating characteristics (AUROC) using the Clinical Hedges dataset. We demonstrate that ML approaches better discriminate between RCTs and non-RCTs than widely used traditional database search filters at all sensitivity levels; our best-performing model also achieved the best results to date for ML in this task (AUROC 0.987, 95% CI, 0.984-0.989). We provide practical guidance on the role of ML in (1) systematic reviews (high-sensitivity strategies) and (2) rapid reviews and clinical question answering (high-precision strategies) together with recommended probability cutoffs for each use case. Finally, we provide open-source software to enable these approaches to be used in practice.


Assuntos
Bases de Dados Bibliográficas , Armazenamento e Recuperação da Informação/métodos , Aprendizado de Máquina , Ensaios Clínicos Controlados Aleatórios como Assunto , Literatura de Revisão como Assunto , Algoritmos , Medicina Baseada em Evidências , Humanos , Armazenamento e Recuperação da Informação/normas , Curva ROC , Sistema de Registros , Reprodutibilidade dos Testes , Ferramenta de Busca , Sensibilidade e Especificidade , Descritores , Máquina de Vetores de Suporte
8.
AMIA Annu Symp Proc ; 2018: 1157-1166, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30815158

RESUMO

SNOMED CT is a large, complex and widely-used terminology. Auditing is part of the life cycle of terminologies. A review of terminologies' content can identify two error categories: commission errors, such as an incorrect parent or attribute relationship, indicating errors in a concept's modeling, and omission errors, such as missing a parent or attribute relationship, representing incomplete modeling of a concept. According to our experience, terminology curators are mostly interested in commission errors. In recent years, a long-term remodeling project has addressed modeling issues in SNOMED CT's Infectious disease and Congenital disease subhierarchies. In this longitudinal study, we investigated a posteriori the efficacy of complex concepts, called overlapping concepts, to identify commission errors during intensive auditing periods and during maintenance periods over several releases. The algorithmic implication is that when auditing resources are scarce, a methodology of auditing first, or only, the overlapping concepts will obtain a higher auditing yield.


Assuntos
Descritores , Systematized Nomenclature of Medicine , Classificação , Estudos Longitudinais , Registros Médicos , Software
9.
J Wound Ostomy Continence Nurs ; 44(3): 277-282, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-28328646

RESUMO

PURPOSE: The objectives of this study were to characterize the odors of used incontinence products by descriptive analysis and to define attributes to be used in the analysis. A further objective was to investigate to what extent the odor profiles of used incontinence products differed from each other and, if possible, to group these profiles into classes. SUBJECTS AND SETTING: Used incontinence products were collected from 14 residents with urinary incontinence living in geriatric nursing homes in the Gothenburg area, Sweden. METHODS: Pieces were cut from the wet area of used incontinence products. They were placed in glass bottles and kept frozen until odor analysis was completed. A trained panel consisting of 8 judges experienced in this area of investigation defined terminology for odor attributes. The intensities of these attributes in the used products were determined by descriptive odor analysis. Data were analyzed both by analysis of variance (ANOVA) followed by the Tukey post hoc test and by principal component analysis and cluster analysis. RESULTS: An odor wheel, with 10 descriptive attributes, was developed. The total odor intensity, and the intensities of the attributes, varied considerably between different, used incontinence products. The typical odors varied from "sweetish" to "urinal," "ammonia," and "smoked." Cluster analysis showed that the used products, based on the quantitative odor data, could be divided into 5 odor classes with different profiles. CONCLUSIONS: The used products varied considerably in odor character and intensity. Findings suggest that odors in used absorptive products are caused by different types of compounds that may vary in concentration.


Assuntos
Tampões Absorventes para a Incontinência Urinária , Odorantes/análise , Percepção , Descritores , Idoso , Idoso de 80 Anos ou mais , Análise por Conglomerados , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Casas de Saúde/organização & administração , Suécia , Incontinência Urinária/enfermagem
10.
J Am Med Inform Assoc ; 24(4): 788-798, 2017 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-28339775

RESUMO

Objective: Quality assurance of large ontological systems such as SNOMED CT is an indispensable part of the terminology management lifecycle. We introduce a hybrid structural-lexical method for scalable and systematic discovery of missing hierarchical relations and concepts in SNOMED CT. Material and Methods: All non-lattice subgraphs (the structural part) in SNOMED CT are exhaustively extracted using a scalable MapReduce algorithm. Four lexical patterns (the lexical part) are identified among the extracted non-lattice subgraphs. Non-lattice subgraphs exhibiting such lexical patterns are often indicative of missing hierarchical relations or concepts. Each lexical pattern is associated with a potential specific type of error. Results: Applying the structural-lexical method to SNOMED CT (September 2015 US edition), we found 6801 non-lattice subgraphs that matched these lexical patterns, of which 2046 were amenable to visual inspection. We evaluated a random sample of 100 small subgraphs, of which 59 were reviewed in detail by domain experts. All the subgraphs reviewed contained errors confirmed by the experts. The most frequent type of error was missing is-a relations due to incomplete or inconsistent modeling of the concepts. Conclusions: Our hybrid structural-lexical method is innovative and proved effective not only in detecting errors in SNOMED CT, but also in suggesting remediation for these errors.


Assuntos
Mineração de Dados/métodos , Descritores , Systematized Nomenclature of Medicine , Garantia da Qualidade dos Cuidados de Saúde
12.
AMIA Annu Symp Proc ; 2017: 364-373, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-29854100

RESUMO

Quality assurance of biomedical terminologies such as the National Cancer Institute (NCI) Thesaurus is an essential part of the terminology management lifecycle. We investigate a structural-lexical approach based on non-lattice subgraphs to automatically identify missing hierarchical relations and missing concepts in the NCI Thesaurus. We mine six structural-lexical patterns exhibiting in non-lattice subgraphs: containment, union, intersection, union-intersection, inference-contradiction, and inference union. Each pattern indicates a potential specific type of error and suggests a potential type of remediation. We found 809 non-lattice subgraphs with these patterns in the NCI Thesaurus (version 16.12d). Domain experts evaluated a random sample of 50 small non-lattice subgraphs, of which 33 were confirmed to contain errors and make correct suggestions (33/50 = 66%). Of the 25 evaluated subgraphs revealing multiple patterns, 22 were verified correct (22/25 = 88%). This shows the effectiveness of our structurallexical-pattern-based approach in detecting errors and suggesting remediations in the NCI Thesaurus.


Assuntos
National Cancer Institute (U.S.) , Vocabulário Controlado , Mineração de Dados , Controle de Qualidade , Descritores , Estados Unidos
14.
J Med Internet Res ; 18(1): e1, 2016 Jan 04.
Artigo em Inglês | MEDLINE | ID: mdl-26728964

RESUMO

BACKGROUND: Conventional Web-based search engines may be unusable by individuals with low health literacy for finding health-related information, thus precluding their use by this population. OBJECTIVE: We describe a conversational search engine interface designed to allow individuals with low health and computer literacy identify and learn about clinical trials on the Internet. METHODS: A randomized trial involving 89 participants compared the conversational search engine interface (n=43) to the existing conventional keyword- and facet-based search engine interface (n=46) for the National Cancer Institute Clinical Trials database. Each participant performed 2 tasks: finding a clinical trial for themselves and finding a trial that met prespecified criteria. RESULTS: Results indicated that all participants were more satisfied with the conversational interface based on 7-point self-reported satisfaction ratings (task 1: mean 4.9, SD 1.8 vs mean 3.2, SD 1.8, P<.001; task 2: mean 4.8, SD 1.9 vs mean 3.2, SD 1.7, P<.001) compared to the conventional Web form-based interface. All participants also rated the trials they found as better meeting their search criteria, based on 7-point self-reported scales (task 1: mean 3.7, SD 1.6 vs mean 2.7, SD 1.8, P=.01; task 2: mean 4.8, SD 1.7 vs mean 3.4, SD 1.9, P<.01). Participants with low health literacy failed to find any trials that satisfied the prespecified criteria for task 2 using the conventional search engine interface, whereas 36% (5/14) were successful at this task using the conversational interface (P=.05). CONCLUSIONS: Conversational agents can be used to improve accessibility to Web-based searches in general and clinical trials in particular, and can help decrease recruitment bias against disadvantaged populations.


Assuntos
Ensaios Clínicos como Assunto , Bases de Dados como Assunto , Letramento em Saúde , Armazenamento e Recuperação da Informação/métodos , Ferramenta de Busca , Descritores , Idoso , Alfabetização Digital , Feminino , Humanos , Internet , Masculino , Pessoa de Meia-Idade , Interface Usuário-Computador
15.
AMIA Annu Symp Proc ; 2016: 618-627, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-28269858

RESUMO

The National Cancer Institute Thesaurus (NCIt) is a reference terminology used to support clinical, translational and basic research as well as administrative activities. As medical knowledge evolves, concepts that might be missing from a particular needed subdomain are regularly added to the NCIt. However, terminology development is known to be labor-intensive and error-prone. Therefore, cost-effective semi-automated methods for identifying potentially missing concepts would be useful to terminology curators. Previously, we have developed a structural method leveraging the native term mappings of the Unified Medical Language System to identify potential concepts in several of its source vocabularies to enrich the SNOMED CT. In this paper, we tested an analogous method for NCIt. Concepts from eight UMLS source terminologies were identified as possibilities to enrich NCIt's conceptual content.


Assuntos
National Cancer Institute (U.S.) , Descritores , Unified Medical Language System , Vocabulário Controlado , Humanos , Neoplasias , Systematized Nomenclature of Medicine , Estados Unidos
16.
AMIA Annu Symp Proc ; 2016: 974-983, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-28269894

RESUMO

SNOMED CT's content undergoes many changes from one release to the next. Over the last year SNOMED CT's Bacterial infectious disease subhierarchy has undergone significant editing to bring consistent modeling to its concepts. In this paper we analyze the stated and inferred structural modifications that affected the Bacterial infectious disease subhierarchy between the Jan 2015 and Jan 2016 SNOMED CT releases using a two-phased approach. First, we introduce a methodology for creating a human readable list of changes. Next, we utilize partial-area taxonomies, which are compact summaries of SNOMED CT's content and structure, to identify the "big picture" changes that occurred in the subhierarchy. We illustrate how partial-area taxonomies can be used to help identify groups of concepts that were affected by these editing operations and the nature of these changes. Modeling issues identified using our two-phase methodology are discussed.


Assuntos
Infecções Bacterianas/classificação , Descritores , Systematized Nomenclature of Medicine , Humanos
17.
Medicina (B Aires) ; 75(5): 289-96, 2015.
Artigo em Espanhol | MEDLINE | ID: mdl-26502463

RESUMO

UNLABELLED: The Mini Clinical Evaluation Exercise (Mini-CEX) is an assessment tool, which emphasizes the educational value and is based on direct performance observation. The objective was to evaluate the reliability and feasibility of Mini-CEX using pediatric descriptors during its implementation in two pediatric residency programs. The design was observational, exploratory and feasibility in the use of this evaluation tool. Based on the original format, descriptors related to the pediatric consult for each Mini-CEX dimension's were agreed. Operators were trained in the use of this tool by means of descriptors as well as in debriefing strategies. Finally, there were two simultaneous and independent evaluations for each observation. ANALYSIS: a) Mini-CEX global and dimension score; b) Concordance between operators scores (mean differences and 95% CI); c) Non evaluable descriptors frequency; d) Duration and satisfaction in use. There were 80 observations in 40 pediatric consults. Overall score 7.5±0.9 (6.4±2 to 8.3±1.1 depending on dimension), with no significant differences between the two institutions. There was high agreement between observers (Mean, difference between 0.1 and 0.3, 95% CI -0.8 to 0.3). The frequency of non evaluable descriptors ranged 5-28 (9% to 51%) and it was not associated with the implementation stage. The average implementation time was 20 minutes, and satisfaction in use was high among both operators and residents. Mini-CEX tool using pediatric descriptors showed high reliability. The joint experience was satisfactory and simultaneously confirmed the value of debriefing.


Assuntos
Avaliação Educacional/métodos , Internato e Residência/métodos , Ambulatório Hospitalar , Pediatria/educação , Indicadores de Qualidade em Assistência à Saúde/estatística & dados numéricos , Competência Clínica/estatística & dados numéricos , Estudos de Viabilidade , Humanos , Variações Dependentes do Observador , Reprodutibilidade dos Testes , Descritores , Fatores de Tempo , Desempenho Profissional/educação
19.
J Am Med Inform Assoc ; 22(3): 628-39, 2015 May.
Artigo em Inglês | MEDLINE | ID: mdl-25332354

RESUMO

OBJECTIVE: Large and complex terminologies, such as Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT), are prone to errors and inconsistencies. Abstraction networks are compact summarizations of the content and structure of a terminology. Abstraction networks have been shown to support terminology quality assurance. In this paper, we introduce an abstraction network derivation methodology which can be applied to SNOMED CT target hierarchies whose classes are defined using only hierarchical relationships (ie, without attribute relationships) and similar description-logic-based terminologies. METHODS: We introduce the tribal abstraction network (TAN), based on the notion of a tribe-a subhierarchy rooted at a child of a hierarchy root, assuming only the existence of concepts with multiple parents. The TAN summarizes a hierarchy that does not have attribute relationships using sets of concepts, called tribal units that belong to exactly the same multiple tribes. Tribal units are further divided into refined tribal units which contain closely related concepts. A quality assurance methodology that utilizes TAN summarizations is introduced. RESULTS: A TAN is derived for the Observable entity hierarchy of SNOMED CT, summarizing its content. A TAN-based quality assurance review of the concepts of the hierarchy is performed, and erroneous concepts are shown to appear more frequently in large refined tribal units than in small refined tribal units. Furthermore, more erroneous concepts appear in large refined tribal units of more tribes than of fewer tribes. CONCLUSIONS: In this paper we introduce the TAN for summarizing SNOMED CT target hierarchies. A TAN was derived for the Observable entity hierarchy of SNOMED CT. A quality assurance methodology utilizing the TAN was introduced and demonstrated.


Assuntos
Classificação , Descritores , Systematized Nomenclature of Medicine , Terminologia como Assunto
20.
J Med Libr Assoc ; 102(3): 177-83, 2014 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-25031558

RESUMO

BACKGROUND: Since 2005, International Committee of Medical Journal Editors (ICMJE) member journals have required that clinical trials be registered in publicly available trials registers before they are considered for publication. OBJECTIVES: The research explores whether it is adequate, when searching to inform systematic reviews, to search for relevant clinical trials using only public trials registers and to identify the optimal search approaches in trials registers. METHODS: A search was conducted in ClinicalTrials.gov and the International Clinical Trials Registry Platform (ICTRP) for research studies that had been included in eight systematic reviews. Four search approaches (highly sensitive, sensitive, precise, and highly precise) were performed using the basic and advanced interfaces in both resources. RESULTS: On average, 84% of studies were not listed in either resource. The largest number of included studies was retrieved in ClinicalTrials.gov and ICTRP when a sensitive search approach was used in the basic interface. The use of the advanced interface maintained or improved sensitivity in 16 of 19 strategies for Clinicaltrials.gov and 8 of 18 for ICTRP. No single search approach was sensitive enough to identify all studies included in the 6 reviews. CONCLUSIONS: Trials registers cannot yet be relied upon as the sole means to locate trials for systematic reviews. Trials registers lag behind the major bibliographic databases in terms of their search interfaces. IMPLICATIONS: For systematic reviews, trials registers and major bibliographic databases should be searched. Trials registers should be searched using sensitive approaches, and both the registers consulted in this study should be searched.


Assuntos
Ensaios Clínicos como Assunto/estatística & dados numéricos , Armazenamento e Recuperação da Informação/métodos , Literatura de Revisão como Assunto , Indexação e Redação de Resumos/estatística & dados numéricos , Medicina Baseada em Evidências , Humanos , Disseminação de Informação , Ensaios Clínicos Controlados Aleatórios como Assunto , Sistema de Registros , Descritores
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