Base de dados : MEDLINE
Pesquisa : L01.453.245.945.700.500 [Categoria DeCS]
Referências encontradas : 557 [refinar]
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[PMID]:28423780
[Au] Autor:Pasche E; Chinali M; Gobeill J; Ruch P
[Ad] Endereço:BiTeM Group, Information Science Department, University of Applied Sciences of Western Switzerland (HES-SO, HEG), Switzerland.
[Ti] Título:Development and Evaluation of a Case-Based Retrieval Service.
[So] Source:Stud Health Technol Inform;235:186-190, 2017.
[Is] ISSN:0926-9630
[Cp] País de publicação:Netherlands
[La] Idioma:eng
[Ab] Resumo:Identifying similar patients might greatly facilitate the treatment of a given patient, enabling to observe the response and outcome to a particular treatment. Case-based retrieval services dealing with natural language processing are of major importance to deal with the significant amount of unstructured clinical data. In this paper, we present the development and evaluation of a case-based retrieval (CBR) service tested on a collection of Italian pediatric cardiology cases. Cases are indexed and a search engine is proposed. Search functionalities, such as interactive MeSH normalization and relevance feedback, are proposed. While the qualitative evaluation aims to provide feedback and recommendations, the quantitative evaluation enables to estimate the precision of the system. In more than half of the cases and for up to two thirds of them, the system is able to suggest a similar episode of care at first rank. With an improvement of the feedback relevance strategy, we can expect an improvement of the precision. The CBR can be expanded to multilingual EHR and other fields.
[Mh] Termos MeSH primário: Registros Eletrônicos de Saúde
Armazenamento e Recuperação da Informação/métodos
Processamento de Linguagem Natural
[Mh] Termos MeSH secundário: Criança
Feminino
Cardiopatias/diagnóstico
Cardiopatias/terapia
Seres Humanos
Itália
Masculino
Medical Subject Headings
Ferramenta de Busca
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1710
[Cu] Atualização por classe:171017
[Lr] Data última revisão:
171017
[Sb] Subgrupo de revista:T
[Da] Data de entrada para processamento:170421
[St] Status:MEDLINE


  2 / 557 MEDLINE  
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[PMID]:28422961
[Au] Autor:Lu Y; Figler B; Huang H; Tu YC; Wang J; Cheng F
[Ad] Endereço:Department of Pharmaceutical Science, College of Pharmacy, University of South Florida, Tampa, Florida, United States of America.
[Ti] Título:Characterization of the mechanism of drug-drug interactions from PubMed using MeSH terms.
[So] Source:PLoS One;12(4):e0173548, 2017.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Identifying drug-drug interaction (DDI) is an important topic for the development of safe pharmaceutical drugs and for the optimization of multidrug regimens for complex diseases such as cancer and HIV. There have been about 150,000 publications on DDIs in PubMed, which is a great resource for DDI studies. In this paper, we introduced an automatic computational method for the systematic analysis of the mechanism of DDIs using MeSH (Medical Subject Headings) terms from PubMed literature. MeSH term is a controlled vocabulary thesaurus developed by the National Library of Medicine for indexing and annotating articles. Our method can effectively identify DDI-relevant MeSH terms such as drugs, proteins and phenomena with high accuracy. The connections among these MeSH terms were investigated by using co-occurrence heatmaps and social network analysis. Our approach can be used to visualize relationships of DDI terms, which has the potential to help users better understand DDIs. As the volume of PubMed records increases, our method for automatic analysis of DDIs from the PubMed database will become more accurate.
[Mh] Termos MeSH primário: Interações Medicamentosas
Medical Subject Headings
Medicamentos sob Prescrição/farmacologia
PubMed/estatística & dados numéricos
[Mh] Termos MeSH secundário: Algoritmos
Sistema Enzimático do Citocromo P-450/metabolismo
Seres Humanos
Curva ROC
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Nm] Nome de substância:
0 (Prescription Drugs); 9035-51-2 (Cytochrome P-450 Enzyme System)
[Em] Mês de entrada:1704
[Cu] Atualização por classe:170505
[Lr] Data última revisão:
170505
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170420
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0173548


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[PMID]:28412964
[Au] Autor:Mao Y; Lu Z
[Ad] Endereço:Nanjing University of Chinese Medicine, 138 Xianlin Avenue, Nanjing, Jiangsu, 210023, China.
[Ti] Título:MeSH Now: automatic MeSH indexing at PubMed scale via learning to rank.
[So] Source:J Biomed Semantics;8(1):15, 2017 Apr 17.
[Is] ISSN:2041-1480
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:BACKGROUND: MeSH indexing is the task of assigning relevant MeSH terms based on a manual reading of scholarly publications by human indexers. The task is highly important for improving literature retrieval and many other scientific investigations in biomedical research. Unfortunately, given its manual nature, the process of MeSH indexing is both time-consuming (new articles are not immediately indexed until 2 or 3 months later) and costly (approximately ten dollars per article). In response, automatic indexing by computers has been previously proposed and attempted but remains challenging. In order to advance the state of the art in automatic MeSH indexing, a community-wide shared task called BioASQ was recently organized. METHODS: We propose MeSH Now, an integrated approach that first uses multiple strategies to generate a combined list of candidate MeSH terms for a target article. Through a novel learning-to-rank framework, MeSH Now then ranks the list of candidate terms based on their relevance to the target article. Finally, MeSH Now selects the highest-ranked MeSH terms via a post-processing module. RESULTS: We assessed MeSH Now on two separate benchmarking datasets using traditional precision, recall and F -score metrics. In both evaluations, MeSH Now consistently achieved over 0.60 in F-score, ranging from 0.610 to 0.612. Furthermore, additional experiments show that MeSH Now can be optimized by parallel computing in order to process MEDLINE documents on a large scale. CONCLUSIONS: We conclude that MeSH Now is a robust approach with state-of-the-art performance for automatic MeSH indexing and that MeSH Now is capable of processing PubMed scale documents within a reasonable time frame. AVAILABILITY: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/MeSHNow/ .
[Mh] Termos MeSH primário: Resumos e Indexação como Assunto/métodos
Aprendizado de Máquina
Medical Subject Headings
PubMed
[Mh] Termos MeSH secundário: Automação
Benchmarking
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1704
[Cu] Atualização por classe:170428
[Lr] Data última revisão:
170428
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170418
[St] Status:MEDLINE
[do] DOI:10.1186/s13326-017-0123-3


  4 / 557 MEDLINE  
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[PMID]:28391809
[Au] Autor:Stewart SA; Abidi SS
[Ad] Endereço:Medical Informatics, Department of Community Health and Epidemiology, Faculty of Medicine, Canada. Electronic address: sam.stewart@dal.ca.
[Ti] Título:Leveraging medical taxonomies to improve knowledge management within online communities of practice: The knowledge maps system.
[So] Source:Comput Methods Programs Biomed;143:121-127, 2017 May.
[Is] ISSN:1872-7565
[Cp] País de publicação:Ireland
[La] Idioma:eng
[Ab] Resumo:BACKGROUND AND OBJECTIVE: Online communities of practice contain a wealth of information, stored in the free text of shared communications between community members. The Knowledge Maps (KMaps) system is designed to facilitate Knowledge Translation in online communities through multi-level analyses of the shared messages of these communications. METHODS: Using state-of-the-art semantic mapping technologies (Metamap) the contents of the messages shared within an online community are mapped to terms from the MeSH medical lexicon, providing a multi-level topic-specific summary of the knowledge being shared within the community. Using the inherent hierarchical structure of the lexicon important insights can be found within the community. RESULTS: The KMaps system was applied to two medical mailing lists, the PPML (archives from 2009-02 to 2013-02) and SURGINET (archives from 2012-01 to 2013-04), identifying 27,924 and 50,597 medical terms respectively. KMaps identified content areas where both communities found interest, specifically around Diseases, 22% and 24% of the total terms, while also identifying field-specific areas that were more popular: SURGINET expressed an interest in Anatomy (14% vs 4%) while the PPML was more interested in Drugs (19% vs 9%). At the level of the individual KMaps identified 6 PPML users and 9 SURGINET users that had noticeably more contributions to the community than their peers, and investigated their personal areas of interest. CONCLUSION: The KMaps system provides valuable insights into the structure of both communities, identifying topics of interest/shared content areas and defining content-profiles for individual community members. The system provides a valuable addition to the online KT process.
[Mh] Termos MeSH primário: Gestão do Conhecimento
Pesquisa Médica Translacional/métodos
[Mh] Termos MeSH secundário: Algoritmos
Comunicação
Pessoal de Saúde
Seres Humanos
Internet
Medical Subject Headings
Medicina
Processamento de Linguagem Natural
Dor
Pediatria
Semântica
Software
Cirurgiões
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1709
[Cu] Atualização por classe:170926
[Lr] Data última revisão:
170926
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170411
[St] Status:MEDLINE


  5 / 557 MEDLINE  
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[PMID]:28231809
[Au] Autor:Mork J; Aronson A; Demner-Fushman D
[Ad] Endereço:US National Library of Medicine, 8600 Rockville Pike, Bethesda, USA. jmork@mail.nlm.nih.gov.
[Ti] Título:12 years on - Is the NLM medical text indexer still useful and relevant?
[So] Source:J Biomed Semantics;8(1):8, 2017 Feb 23.
[Is] ISSN:2041-1480
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:BACKGROUND: Facing a growing workload and dwindling resources, the US National Library of Medicine (NLM) created the Indexing Initiative project in 1996. This cross-library team's mission is to explore indexing methodologies for ensuring quality and currency of NLM document collections. The NLM Medical Text Indexer (MTI) is the main product of this project and has been providing automated indexing recommendations since 2002. After all of this time, the questions arise whether MTI is still useful and relevant. METHODS: To answer the question about MTI usefulness, we track a wide variety of statistics related to how frequently MEDLINE indexers refer to MTI recommendations, how well MTI performs against human indexing, and how often MTI is used. To answer the question of MTI relevancy compared to other available tools, we have participated in the 2013 and 2014 BioASQ Challenges. The BioASQ Challenges have provided us with an unbiased comparison between the MTI system and other systems performing the same task. RESULTS: Indexers have continually increased their use of MTI recommendations over the years from 15.75% of the articles they index in 2002 to 62.44% in 2014 showing that the indexers find MTI to be increasingly useful. The MTI performance statistics show significant improvement in Precision (+0.2992) and F (+0.1997) with modest gains in Recall (+0.0454) over the years. MTI consistency is comparable to the available indexer consistency studies. MTI performed well in both of the BioASQ Challenges ranking within the top tier teams. CONCLUSIONS: Based on our findings, yes, MTI is still relevant and useful, and needs to be improved and expanded. The BioASQ Challenge results have shown that we need to incorporate more machine learning into MTI while still retaining the indexing rules that have earned MTI the indexers' trust over the years. We also need to expand MTI through the use of full text, when and where it is available, to provide coverage of indexing terms that are typically only found in the full text. The role of MTI at NLM is also expanding into new areas, further reinforcing the idea that MTI is increasingly useful and relevant.
[Mh] Termos MeSH primário: Resumos e Indexação como Assunto
National Library of Medicine (U.S.)
[Mh] Termos MeSH secundário: Seres Humanos
Aprendizado de Máquina
Medical Subject Headings
Estados Unidos
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1711
[Cu] Atualização por classe:171106
[Lr] Data última revisão:
171106
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170225
[St] Status:MEDLINE
[do] DOI:10.1186/s13326-017-0113-5


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[PMID]:28028289
[Au] Autor:Huh S
[Ad] Endereço:Department of Parasitology and Institute of Medical Education, Hallym University College of Medicine, Chuncheon, Korea.
[Ti] Título:An eventful year for the .
[So] Source:J Educ Eval Health Prof;13:45, 2017.
[Is] ISSN:1975-5937
[Cp] País de publicação:Korea (South)
[La] Idioma:eng
[Mh] Termos MeSH primário: Ocupações em Saúde/educação
Medical Subject Headings
Publicações Periódicas como Assunto
[Mh] Termos MeSH secundário: Acesso à Informação
Seres Humanos
MEDLINE
Estados Unidos
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1705
[Cu] Atualização por classe:170512
[Lr] Data última revisão:
170512
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:161229
[St] Status:MEDLINE
[do] DOI:10.3352/jeehp.2016.13.45


  7 / 557 MEDLINE  
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[PMID]:27679477
[Au] Autor:Yang S; Kim CY; Hwang S; Kim E; Kim H; Shim H; Lee I
[Ad] Endereço:Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Korea.
[Ti] Título:COEXPEDIA: exploring biomedical hypotheses via co-expressions associated with medical subject headings (MeSH).
[So] Source:Nucleic Acids Res;45(D1):D389-D396, 2017 Jan 04.
[Is] ISSN:1362-4962
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:The use of high-throughput array and sequencing technologies has produced unprecedented amounts of gene expression data in central public depositories, including the Gene Expression Omnibus (GEO). The immense amount of expression data in GEO provides both vast research opportunities and data analysis challenges. Co-expression analysis of high-dimensional expression data has proven effective for the study of gene functions, and several co-expression databases have been developed. Here, we present a new co-expression database, COEXPEDIA (www.coexpedia.org), which is distinctive from other co-expression databases in three aspects: (i) it contains only co-functional co-expressions that passed a rigorous statistical assessment for functional association, (ii) the co-expressions were inferred from individual studies, each of which was designed to investigate gene functions with respect to a particular biomedical context such as a disease and (iii) the co-expressions are associated with medical subject headings (MeSH) that provide biomedical information for anatomical, disease, and chemical relevance. COEXPEDIA currently contains approximately eight million co-expressions inferred from 384 and 248 GEO series for humans and mice, respectively. We describe how these MeSH-associated co-expressions enable the identification of diseases and drugs previously unknown to be related to a gene or a gene group of interest.
[Mh] Termos MeSH primário: Biologia Computacional/métodos
Bases de Dados Genéticas
Medical Subject Headings
[Mh] Termos MeSH secundário: Perfilação da Expressão Gênica/métodos
Regulação da Expressão Gênica
Predisposição Genética para Doença
Estudo de Associação Genômica Ampla/métodos
Seres Humanos
Software
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1706
[Cu] Atualização por classe:170615
[Lr] Data última revisão:
170615
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:160929
[St] Status:MEDLINE
[do] DOI:10.1093/nar/gkw868


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[PMID]:27215603
[Au] Autor:Irwin AN; Rackham D
[Ad] Endereço:Oregon State University College of Pharmacy, 203 Pharmacy, Corvallis, OR 97331, USA. Electronic address: Adriane.irwin@oregonstate.edu.
[Ti] Título:Comparison of the time-to-indexing in PubMed between biomedical journals according to impact factor, discipline, and focus.
[So] Source:Res Social Adm Pharm;13(2):389-393, 2017 Mar - Apr.
[Is] ISSN:1934-8150
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:BACKGROUND: Practicing evidence-based medicine requires health care professionals to efficiently retrieve relevant and current literature. OBJECTIVE: The purpose of this study was to compare the time interval between PubMed entry and indexing with Medical Subject Headings (MeSH) between biomedical journals with varying impact factors, focus areas, and health care discipline representation. METHODS: This was a cross-sectional study of articles entered into PubMed database between January 1 and December 31, 2012. The primary endpoint was the number of days between PubMed entry and indexing with MeSH terms. RESULTS: A total of 7906 articles were reviewed across 18 journals. In the first comparison, the time-to-indexing was 177 ± 100 days, 111 ± 69 days, and 23 ± 40 days for articles published in journals with impact factors of 2.0-2.5, 4.5-6.5, and >25, respectively (P ≤ 0.001). In the second comparison, the time-to-indexing was 111 ± 69 days for general medicine versus 170 ± 74 days for specialty journals (P ≤ 0.001). In the third comparison, the overall time-to-indexing was 177 ± 100 days, 234 ± 107 days, and 163 ± 58 days for medicine, nursing, and pharmacy journals, respectively (P ≤ 0.001). CONCLUSIONS: Study results identified a significant delay between entry of articles into the PubMed database and time-to-indexing with MeSH terms across journals of varying impact factor, discipline, and focus. Results suggest that there may be factors that influence the priority by which articles are indexed with MeSH terms. Future research should focus on determining those journal characteristics and any impact of this delay on clinical practice.
[Mh] Termos MeSH primário: Resumos e Indexação como Assunto/estatística & dados numéricos
Fator de Impacto de Revistas
Publicações Periódicas como Assunto/estatística & dados numéricos
PubMed/estatística & dados numéricos
[Mh] Termos MeSH secundário: Estudos Transversais
Medicina Baseada em Evidências
Seres Humanos
Medical Subject Headings
Fatores de Tempo
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1709
[Cu] Atualização por classe:170904
[Lr] Data última revisão:
170904
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:160525
[St] Status:MEDLINE


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[PMID]:27935993
[Au] Autor:Ho GJ; Liew SM; Ng CJ; Hisham Shunmugam R; Glasziou P
[Ad] Endereço:Department of Primary Care Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
[Ti] Título:Development of a Search Strategy for an Evidence Based Retrieval Service.
[So] Source:PLoS One;11(12):e0167170, 2016.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:BACKGROUND: Physicians are often encouraged to locate answers for their clinical queries via an evidence-based literature search approach. The methods used are often not clearly specified. Inappropriate search strategies, time constraint and contradictory information complicate evidence retrieval. AIMS: Our study aimed to develop a search strategy to answer clinical queries among physicians in a primary care setting. METHODS: Six clinical questions of different medical conditions seen in primary care were formulated. A series of experimental searches to answer each question was conducted on 3 commonly advocated medical databases. We compared search results from a PICO (patients, intervention, comparison, outcome) framework for questions using different combinations of PICO elements. We also compared outcomes from doing searches using text words, Medical Subject Headings (MeSH), or a combination of both. All searches were documented using screenshots and saved search strategies. RESULTS: Answers to all 6 questions using the PICO framework were found. A higher number of systematic reviews were obtained using a 2 PICO element search compared to a 4 element search. A more optimal choice of search is a combination of both text words and MeSH terms. Despite searching using the Systematic Review filter, many non-systematic reviews or narrative reviews were found in PubMed. There was poor overlap between outcomes of searches using different databases. The duration of search and screening for the 6 questions ranged from 1 to 4 hours. CONCLUSION: This strategy has been shown to be feasible and can provide evidence to doctors' clinical questions. It has the potential to be incorporated into an interventional study to determine the impact of an online evidence retrieval system.
[Mh] Termos MeSH primário: Medicina Baseada em Evidências/métodos
Armazenamento e Recuperação da Informação/métodos
Assistência ao Paciente/métodos
Médicos
[Mh] Termos MeSH secundário: Sistemas de Computação
Bases de Dados Bibliográficas
Medicina Baseada em Evidências/normas
Medicina Baseada em Evidências/estatística & dados numéricos
Seres Humanos
Informática Médica/métodos
Informática Médica/normas
Informática Médica/estatística & dados numéricos
Medical Subject Headings
Sistemas On-Line
PubMed
Reprodutibilidade dos Testes
Inquéritos e Questionários
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1707
[Cu] Atualização por classe:170726
[Lr] Data última revisão:
170726
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:161210
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0167170


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[PMID]:27930574
[Au] Autor:Shan G; Lu Y; Min B; Qu W; Zhang C
[Ad] Endereço:Beijing Institute of Radiation Medicine, State Key Laboratory of Proteomics, Cognitive and Mental Health Research Center, Beijing, PR China.
[Ti] Título:A MeSH-based text mining method for identifying novel prebiotics.
[So] Source:Medicine (Baltimore);95(49):e5585, 2016 Dec.
[Is] ISSN:1536-5964
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Prebiotics contribute to the well-being of their host by altering the composition of the gut microbiota. Discovering new prebiotics is a challenging and arduous task due to strict inclusion criteria; thus, highly limited numbers of prebiotic candidates have been identified. Notably, the large numbers of published studies may contain substantial information attached to various features of known prebiotics that can be used to predict new candidates. In this paper, we propose a medical subject headings (MeSH)-based text mining method for identifying new prebiotics with structured texts obtained from PubMed. We defined an optimal feature set for prebiotics prediction using a systematic feature-ranking algorithm with which a variety of carbohydrates can be accurately classified into different clusters in accordance with their chemical and biological attributes. The optimal feature set was used to separate positive prebiotics from other carbohydrates, and a cross-validation procedure was employed to assess the prediction accuracy of the model. Our method achieved a specificity of 0.876 and a sensitivity of 0.838. Finally, we identified a high-confidence list of candidates of prebiotics that are strongly supported by the literature. Our study demonstrates that text mining from high-volume biomedical literature is a promising approach in searching for new prebiotics.
[Mh] Termos MeSH primário: Mineração de Dados/métodos
Medical Subject Headings/utilização
Probióticos/farmacologia
[Mh] Termos MeSH secundário: Probióticos/uso terapêutico
Reprodutibilidade dos Testes
[Pt] Tipo de publicação:COMPARATIVE STUDY; JOURNAL ARTICLE; OBSERVATIONAL STUDY
[Em] Mês de entrada:1702
[Cu] Atualização por classe:170224
[Lr] Data última revisão:
170224
[Sb] Subgrupo de revista:AIM; IM
[Da] Data de entrada para processamento:161209
[St] Status:MEDLINE



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