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1.
Artif Intell Med ; 114: 102053, 2021 04.
Artigo em Inglês | MEDLINE | ID: mdl-33875160

RESUMO

MOTIVATION: In the age of big data, the amount of scientific information available online dwarfs the ability of current tools to support researchers in locating and securing access to the necessary materials. Well-structured open data and the smart systems that make the appropriate use of it are invaluable and can help health researchers and professionals to find the appropriate information by, e.g., configuring the monitoring of information or refining a specific query on a disease. METHODS: We present an automated text classifier approach based on the MEDLINE/MeSH thesaurus, trained on the manual annotation of more than 26 million expert-annotated scientific abstracts. The classifier was developed tailor-fit to the public health and health research domain experts, in the light of their specific challenges and needs. We have applied the proposed methodology on three specific health domains: the Coronavirus, Mental Health and Diabetes, considering the pertinence of the first, and the known relations with the other two health topics. RESULTS: A classifier is trained on the MEDLINE dataset that can automatically annotate text, such as scientific articles, news articles or medical reports with relevant concepts from the MeSH thesaurus. CONCLUSIONS: The proposed text classifier shows promising results in the evaluation of health-related news. The application of the developed classifier enables the exploration of news and extraction of health-related insights, based on the MeSH thesaurus, through a similar workflow as in the usage of PubMed, with which most health researchers are familiar.


Assuntos
Comunicação em Saúde/normas , MEDLINE/organização & administração , Medical Subject Headings , Pesquisa/organização & administração , Big Data , Classificação , Diabetes Mellitus/epidemiologia , Humanos , MEDLINE/normas , Saúde Mental/estatística & dados numéricos , Semântica
2.
Medicine (Baltimore) ; 100(6): e24610, 2021 Feb 12.
Artigo em Inglês | MEDLINE | ID: mdl-33578568

RESUMO

BACKGROUND: Comparison of similarity and difference in research types among journals are concerned in literature. However, to date, none display the methodology seen in selecting similar journals related to the target journal, as similar articles did to a given article. Authors need 1 effective method not only to find similar journals for their studies but also to know the difference in methods. This study (1) shows the similar journals for the target journal online displayed, and (2) identifies the effect of similarity odds ratio compared to the counterparts using the forest plots in Meta-analysis and the major medical subject headings (MeSH terms). METHODS: We downloaded 1000 recent top 20 most similar articles related to the Respiratory Care journal from the PubMed library, plotted the clusters of related journals using social network analysis (SNA), and compared the MeSH terms in differences in an odds ratio unit using the forest plot relevant to Respiratory Care and the most similar journals. Q statistic and I-square (I2) index were used to evaluate the difference in the proportion of events. RESULTS: This study found that (1) the journals related to Respiratory Care are easily presented on Google Maps; (2) 10 journal clusters were identified using SNA; (3) the top 3 MeSH terms are methods, therapy, and physiopathology, and (4) the odds ratios of MeSH terms between journals associated with the Respiratory Care showing different from Int J Chron Obstruct Pulmori Dis and similar to Curr Opin Endocrinol Diabetes Obes within heterogeneity with I2 = 70.5% (P < 0.001) and 0% (P = 0.803), respectively. CONCLUSIONS: SNA and forest plots provide deep insight into the relationships between journals in MeSH terms. The results of this research can provide readers with a concept diagram that can be used for future submissions to a given journal.


Assuntos
Bibliometria , Medical Subject Headings , Humanos
3.
Rev. cienc. cuidad ; 18(1): 30-41, 2021.
Artigo em Espanhol | LILACS, BDENF - Enfermagem, COLNAL | ID: biblio-1147584

RESUMO

La administración de medicamentos es uno de los pilares fundamen-tales en la gestión del cuidado de la enfermería, de ahí la impor-tancia y responsabilidad de su manejo con personas que estén re-cibiendo fármacos especializados por su condición aguda o crítica. Objetivo: Construir un instrumento de valoración con los principios homeodinámicos y el concepto de pandimensionalidad de la teoría de seres humanos unitarios, para la administración de medicamen-tos especializados, como inotrópicos y vasopresores en servicios de UCI y urgencias. Materiales y métodos: Es una investigación cua-litativa con enfoque descriptivo, para conocer la realidad del sujeto de cuidado desde la premisa sobre la relación de la administración de medicamentos y los principios de la teoría de Seres Humanos Unitarios. Resultados: Se construyó un instrumento de valoración desde la perspectiva de la teoría de Rogers. La misma permite al profesional de enfermería evaluar y generar propuestas de cuida-do guiadas al equilibrio armónico de los campos de energía, según propone esta teórica en relación con la administración de medica-mentos. También le facilita al estudiante el identificar las alteracio-nes de los principios de la teoría en relación a los efectos de los medicamentos. Conclusiones: El instrumento permite contemplar los principios homeodinámicos y la pandimensionalidad; además, facilita los procesos de cuidado en la administración de medicamen-tos, que en ocasiones por diferentes causas no se evalúa la respuesta del sujeto de cuidado ante un medicamento, retrasando su evolución ante la enfermedad.


The administration of medications is one of the fundamental pillars in the management of nursing care, hence the importance and responsibility of the management of medications to people who are receiving specialized medicine for their acute or critical condition, that is why this study was proposed The objective: is to build an assessment instrument with the homeodynamic principles and the concept of pandimensionality of the theory of unitary human beings for the administration of specialized drugs such as inotropics and vasopressors in ICU and emergency health services. Materials and methods: qualitative with a descriptive approach, to know the reality of the care subject from the premise of the relationship of the administration of medicines and principles of the theory of Unitary Human Beings, Results: an assessment instrument was built from the pers-pective of Rogers' theory that allows the nursing professional to evaluate and generate proposals for care guided to the harmonic balance of the energy fields proposed by this theory in relation to the administration of medications, also, it allows the student to identify alterations in the prin-ciples of the theory in relation to the effects of medications Conclusions: the instrument allows to contemplate homeodynamic principles and the concept of pandimensionality thus facilitate the process of care for the administration of drugs that sometimes for different reasons does not evaluate the response of the care subject to a drug, delaying the evolution of the subject before the disease.


A administração de medicamentos é um dos eixos fundamentais do cuidado de enfermagem, sendo importante a responsabilidade do seu manejo com pessoas que recebem fármacos espe-cializados pela sua condição aguda ou crítica. Objetivo: Construir um instrumento de avaliação dos princípios homeodinâmicos e o conceito de pandimensionalidade da teoria do ser humanos unitários, para a administração de medicamentos especializados como inotrópicos e vasopres-sores nas UTI e Unidades de Emergência. Materiais e métodos: Pesquisa qualitativa com foco descritivo para conhecer a realidade do sujeito de cuidado sobre a relação da administração de medicamentos e os princípios da teoria do Ser Humano Unitário. Resultados: Construiu-se um instrumento de valoração desde a perspectiva de Rogers, permitindo ao profissional de enferma-gem avaliar e gerar propostas de cuidado guiadas ao equilíbrio harmônico dos campos de energia, segundo a proposta da teoria em relação com a administração de medicamentos. Também lhe fa-cilita ao estudante identificar as alterações dos princípios da teoria em relação com os efeitos dos medicamentos. Conclusões: O instrumento permite contemplar os princípios homeodinâmicos e pandimensionalidade; além disso, facilita os processos de cuidado na administração de medica-mentos que ocasionalmente, por diferentes causas não se avalia a resposta do sujeito de cuidado ante um medicamento, retrasando a sua evolução perante a doença.


Assuntos
Enfermagem de Cuidados Críticos , Terapêutica , Medical Subject Headings
4.
BMC Bioinformatics ; 21(Suppl 19): 572, 2020 Dec 21.
Artigo em Inglês | MEDLINE | ID: mdl-33349237

RESUMO

BACKGROUND: Finding relevant literature is crucial for many biomedical research activities and in the practice of evidence-based medicine. Search engines such as PubMed provide a means to search and retrieve published literature, given a query. However, they are limited in how users can control the processing of queries and articles-or as we call them documents-by the search engine. To give this control to both biomedical researchers and computer scientists working in biomedical information retrieval, we introduce a public online tool for searching over biomedical literature. Our setup is guided by the NIST setup of the relevant TREC evaluation tasks in genomics, clinical decision support, and precision medicine. RESULTS: To provide benchmark results for some of the most common biomedical information retrieval strategies, such as querying MeSH subject headings with a specific weight or querying over the title of the articles only, we present our evaluations on public datasets. Our experiments report well-known information retrieval metrics such as precision at a cutoff of ranked documents. CONCLUSIONS: We introduce the A2A search and benchmarking tool which is publicly available for the researchers who want to explore different search strategies over published biomedical literature. We outline several query formulation strategies and present their evaluations with known human judgements for a large pool of topics, from genomics to precision medicine.


Assuntos
Armazenamento e Recuperação da Informação/métodos , Software , Pesquisa Biomédica , Bases de Dados Factuais , Humanos , Medical Subject Headings
5.
BMC Med Inform Decis Mak ; 20(Suppl 14): 306, 2020 12 15.
Artigo em Inglês | MEDLINE | ID: mdl-33323109

RESUMO

BACKGROUND: Automated summarization of scientific literature and patient records is essential for enhancing clinical decision-making and facilitating precision medicine. Most existing summarization methods are based on single indicators of relevance, offer limited capabilities for information visualization, and do not account for user specific interests. In this work, we develop an interactive content extraction, recognition, and construction system (CERC) that combines machine learning and visualization techniques with domain knowledge for highlighting and extracting salient information from clinical and biomedical text. METHODS: A novel sentence-ranking framework multi indicator text summarization, MINTS, is developed for extractive summarization. MINTS uses random forests and multiple indicators of importance for relevance evaluation and ranking of sentences. Indicative summarization is performed using weighted term frequency-inverse document frequency scores of over-represented domain-specific terms. A controlled vocabulary dictionary generated using MeSH, SNOMED-CT, and PubTator is used for determining relevant terms. 35 full-text CRAFT articles were used as the training set. The performance of the MINTS algorithm is evaluated on a test set consisting of the remaining 32 full-text CRAFT articles and 30 clinical case reports using the ROUGE toolkit. RESULTS: The random forests model classified sentences as "good" or "bad" with 87.5% accuracy on the test set. Summarization results from the MINTS algorithm achieved higher ROUGE-1, ROUGE-2, and ROUGE-SU4 scores when compared to methods based on single indicators such as term frequency distribution, position, eigenvector centrality (LexRank), and random selection, p < 0.01. The automatic language translator and the customizable information extraction and pre-processing pipeline for EHR demonstrate that CERC can readily be incorporated within clinical decision support systems to improve quality of care and assist in data-driven and evidence-based informed decision making for direct patient care. CONCLUSIONS: We have developed a web-based summarization and visualization tool, CERC ( https://newton.isye.gatech.edu/CERC1/ ), for extracting salient information from clinical and biomedical text. The system ranks sentences by relevance and includes features that can facilitate early detection of medical risks in a clinical setting. The interactive interface allows users to filter content and edit/save summaries. The evaluation results on two test corpuses show that the newly developed MINTS algorithm outperforms methods based on single characteristics of importance.


Assuntos
Armazenamento e Recuperação da Informação , Medical Subject Headings , Algoritmos , Humanos , Aprendizado de Máquina , Processamento de Linguagem Natural , Vocabulário Controlado
6.
Medicine (Baltimore) ; 99(44): e22885, 2020 Oct 30.
Artigo em Inglês | MEDLINE | ID: mdl-33126338

RESUMO

BACKGROUND: Publications regarding the 100 top-cited articles in a given discipline are common, but studies reporting the association between article topics and their citations are lacking. Whether or not reviews and original articles have a higher impact factor than case reports is a point for verification in this study. In addition, article topics that can be used for predicting citations have not been analyzed. Thus, this study aims to METHODS:: We searched PubMed Central and downloaded 100 top-cited abstracts in the journal Medicine (Baltimore) since 2011. Four article types and 7 topic categories (denoted by MeSH terms) were extracted from abstracts. Contributors to these 100 top-cited articles were analyzed. Social network analysis and Sankey diagram analysis were performed to identify influential article types and topic categories. MeSH terms were applied to predict the number of article citations. We then examined the prediction power with the correlation coefficients between MeSH weights and article citations. RESULTS: The citation counts for the 100 articles ranged from 24 to 127, with an average of 39.1 citations. The most frequent article types were journal articles (82%) and comparative studies (10%), and the most frequent topics were epidemiology (48%) and blood and immunology (36%). The most productive countries were the United States (24%) and China (23%). The most cited article (PDID = 27258521) with a count of 135 was written by Dr Shang from Shandong Provincial Hospital Affiliated to Shandong University (China) in 2016. MeSH terms were evident in the prediction power of the number of article citations (correlation coefficients  = 0.49, t = 5.62). CONCLUSION: The breakthrough was made by developing dashboards showing the overall concept of the 100 top-cited articles using the Sankey diagram. MeSH terms can be used for predicting article citations. Analyzing the 100 top-cited articles could help future academic pursuits and applications in other academic disciplines.


Assuntos
Bibliometria , Fator de Impacto de Revistas , Medical Subject Headings , Publicações Periódicas como Assunto/tendências , Publicações , Previsões , Humanos , Redes Sociais Online , PubMed , Publicações/classificação , Publicações/normas , Publicações/estatística & dados numéricos
7.
Artigo em Espanhol | PAHO-IRIS | ID: phr-52558

RESUMO

[RESUMEN]. El vocabulario Descriptores en Ciencias de la Salud (DeCS) establece un lenguaje único y común que permite la organización y facilita la búsqueda y recuperación de la literatura técnica y científica en salud disponible en las fuentes de información de la Biblioteca Virtual en Salud. El DeCS, creado por el Centro Latinoamericano y del Caribe de Información en Ciencias de la Salud (BIREME), un centro especializado de la Organización Panamericana de la Salud/Organización Mundial de la Salud (OPS/OMS), es la traducción y la extensión del vocabulario Medical Subject Headings (MeSH), mantenido por la National Library of Medicine de los Estados Unidos. BIREME, en coordinación con expertos de América Latina y el Caribe, ha incluido en el DeCS los temas de equidad, género, etnicidad y derechos humanos —temas transversales en el marco programático de la cooperación técnica de la OPS/OMS— para garantizar una mejor recuperación y uso de la información y evidencia científica relacionadas a estos temas. El objetivo de este artículo es describir el método de revisión terminológica del DeCS e informar los resultados obtenidos y los impactos de la ampliación terminológica en el área de equidad, que comprendió la inclusión de 35 nuevos descriptores.


[ABSTRACT]. The Health Sciences Descriptors (DeCS) vocabulary establishes a unique and common language that allows the organization and facilitates the search and retrieval of technical and scientific literature on health available in the information sources of the Virtual Health Library. The DeCS, created by the Latin American and Caribbean Center on Health Sciences Information (BIREME), a specialized center of the Pan American Health Organization/World Health Organization (PAHO/WHO), is the translation and extension of the Medical Subject Headings (MeSH) vocabulary, maintained by the United States National Library of Medicine. BIREME, in coordination with experts from Latin America and the Caribbean, has included in the DeCS the topics of equity, gender, ethnicity and human rights—cross-cutting themes in the programmatic framework of PAHO/WHO technical cooperation—to ensure better retrieval and use of scientific information and evidence related to these topics. The objective of this article is to describe the methodology used during the terminology review of the DeCS and to report the results obtained and the impacts of the terminology expansion in the field of equity, which included the inclusion of 35 new descriptors.


Assuntos
Equidade , Sistemas de Informação , Medical Subject Headings , Prática Clínica Baseada em Evidências , Acesso à Informação , Equidade , Sistemas de Informação , Prática Clínica Baseada em Evidências , Acesso à Informação
8.
PLoS One ; 15(9): e0239694, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32997699

RESUMO

With the novel COVID-19 pandemic disrupting and threatening the lives of millions, researchers and clinicians have been recently conducting clinical trials at an unprecedented rate to learn more about the virus and potential drugs/treatments/vaccines to treat its infection. As a result of the influx of clinical trials, researchers, clinicians, and the lay public, now more than ever, face a significant challenge in keeping up-to-date with the rapid rate of discoveries and advances. To remedy this problem, this research mined the ClinicalTrials.gov corpus to extract COVID-19 related clinical trials, produce unique reports to summarize findings and make the meta-data available via Application Programming Interfaces (APIs). Unique reports were created for each drug/intervention, Medical Subject Heading (MeSH) term, and Human Phenotype Ontology (HPO) term. These reports, which have been run over multiple time points, along with APIs to access meta-data, are freely available at http://covidresearchtrials.com. The pipeline, reports, association of COVID-19 clinical trials with MeSH and HPO terms, insights, public repository, APIs, and correlations produced are all novel in this work. The freely available, novel resources present up-to-date relevant biological information and insights in a robust, accessible manner, illustrating their invaluable potential to aid researchers overcome COVID-19 and save hundreds of thousands of lives.


Assuntos
Ontologias Biológicas , Ensaios Clínicos como Assunto , Infecções por Coronavirus/terapia , Processamento de Linguagem Natural , Pneumonia Viral/terapia , Betacoronavirus , Biologia Computacional , Humanos , Internet , Medical Subject Headings , Pandemias , Fenótipo , Software
9.
Medicine (Baltimore) ; 99(38): e22190, 2020 Sep 18.
Artigo em Inglês | MEDLINE | ID: mdl-32957346

RESUMO

Researchers seek to identify optimal journals for submission based on their studies but tend to rely on journal impact factors or scientific journal rankings. We investigated research trends by selecting high-frequency words from author keywords (AKs), analyzing subject areas, and performing quantitative data analysis of Korean dental journals. Consequently, we suggest a method for choosing journals that fit a specific subject area.We used a corpus of 9 Korean dentistry journals regarded in Korea as quality internationally approved journals. AKs occurring more than 10 times were assigned to Medical Subject Headings (MeSH) terms and subcategories, which were then categorized using the MeSH tree structure. KnowledgeMatrix Plus and VOSviewer were used to analyze network relationships, density, and clustering.The AKs were of 7527 types, 15,960 terms, and formed 54 clusters. The AKs with 10+ occurrence were 199 types, 4289 terms, and formed 9 clusters. Assigning the AKs with 10+ occurrence to MeSH terms led to expanding 732 types of AK terms into 249 types with 9 clusters and 4268 links. Core study areas over the past 10 years were facial asymmetry, a topic under oral surgery and medicine, and orthognathic surgery focused on mandibular fractures, followed by shear bond strength of zirconia. Analyzing 16 MeSH subject categories, we found that the "analytical, diagnostic and therapeutic techniques and equipment" category had the largest distribution of AKs (40.7%). This was followed by "diseases" (21.2%) and "anatomy" (14.90%). The orthognathic surgery cluster was the largest, followed by the shear bond strength cluster. Dental implants is a core area with strong links to high-occurrence words, such as cone-beam computed tomography and mandible, which were distributed in the order of The Journal of Advanced Prosthodontics (37.8%) and Journal of Periodontal & Implant Science (30.6%). Five clusters were closely packed in the center, 2 clusters were formed above the center, 1 cluster was formed below the center, and a cluster on the right was widespread.Cluster analysis using AKs and MeSH may be a good analytic method for researchers to determine expanding research areas and select optimal journals for paper submission.


Assuntos
Bibliometria , Odontologia , Medical Subject Headings , Publicações Periódicas como Assunto , República da Coreia
10.
Artigo em Inglês | MEDLINE | ID: mdl-32698499

RESUMO

Climate change is a challenge for the sustainable development of an international economy and society. The impact of climate change on infectious diseases has been regarded as one of the most urgent research topics. In this paper, an analysis of the bibliometrics, co-word biclustering, and strategic diagram was performed to evaluate global scientific production, hotspots, and developing trends regarding climate change and infectious diseases, based on the data of two decades (1999-2008 and 2009-2018) from PubMed. According to the search strategy and inclusion criteria, a total of 1443 publications were found on the topic of climate change and infectious diseases. There has been increasing research productivity in this field, which has been supported by a wide range of subject categories. The top highly-frequent major MeSH (medical subject headings)/subheading combination terms could be divided into four clusters for the first decade and five for the second decade using a biclustering analysis. At present, some significant public health challenges (global health, and travel and tropical climate, etc.) are at the center of the whole target research network. In the last ten years, "Statistical model", "Diarrhea", "Dengue", "Ecosystem and biodiversity", and "Zoonoses" have been considered as emerging hotspots, but they still need more attention for further development.


Assuntos
Bibliometria , Mudança Climática , Doenças Transmissíveis , Ecossistema , Publicações , Humanos , Medical Subject Headings , Publicações Periódicas como Assunto , PubMed
11.
Stud Health Technol Inform ; 270: 208-212, 2020 Jun 16.
Artigo em Inglês | MEDLINE | ID: mdl-32570376

RESUMO

This paper presents five document retrieval systems for a small (few thousands) and domain specific corpora (weekly peer-reviewed medical journals published in French) as well as an evaluation methodology to quantify the models performance. The proposed methodology does not rely on external annotations and therefore can be used as an ad hoc evaluation procedure for most document retrieval tasks. Statistical models and vector space models are empirically compared on a synthetic document retrieval task. For our dataset size and specificities the statistical approaches consistently performed better than its vector space counterparts.


Assuntos
Armazenamento e Recuperação da Informação/métodos , Idioma , Medical Subject Headings , Modelos Estatísticos , Processamento de Linguagem Natural , Humanos
12.
Stud Health Technol Inform ; 270: 1235-1236, 2020 Jun 16.
Artigo em Inglês | MEDLINE | ID: mdl-32570596

RESUMO

The aim of this study was to examine existing methods for sharing results of stem cell research via online data repositories. To identify the relevant repositories, a PubMed search was conducted using standard MeSH terms which was followed by a web-based search of relevant databases. The search yielded 16 databases created between 2010 and 2019. The review of databases identified 35 major rubrics and their sub-rubrics organized in a five-module system. Data integration approaches were characterized by three domains (common data elements, data visualization and analysis tools, and ontology mapping) which varied widely across the databases. Current state of stem cell data integration lacks reproducibility and standardization. Standardization of data integration approaches for representing stem cell studies is necessary to facilitate data sharing.


Assuntos
Células-Tronco , Bases de Dados Factuais , Disseminação de Informação , Medical Subject Headings , PubMed , Reprodutibilidade dos Testes
13.
BMC Bioinformatics ; 21(1): 252, 2020 Jun 18.
Artigo em Inglês | MEDLINE | ID: mdl-32552728

RESUMO

BACKGROUND: Many disease causing genes have been identified through different methods, but there have been no uniform annotations of biomedical named entity (bio-NE) of the disease phenotypes of these genes yet. Furthermore, semantic similarity comparison between two bio-NE annotations has become important for data integration or system genetics analysis. RESULTS: The package pyMeSHSim recognizes bio-NEs by using MetaMap which produces Unified Medical Language System (UMLS) concepts in natural language process. To map the UMLS concepts to Medical Subject Headings (MeSH), pyMeSHSim is embedded with a house-made dataset containing the main headings (MHs), supplementary concept records (SCRs), and their relations in MeSH. Based on the dataset, pyMeSHSim implemented four information content (IC)-based algorithms and one graph-based algorithm to measure the semantic similarity between two MeSH terms. To evaluate its performance, we used pyMeSHSim to parse OMIM and GWAS phenotypes. The pyMeSHSim introduced SCRs and the curation strategy of non-MeSH-synonymous UMLS concepts, which improved the performance of pyMeSHSim in the recognition of OMIM phenotypes. In the curation of 461 GWAS phenotypes, pyMeSHSim showed recall > 0.94, precision > 0.56, and F1 > 0.70, demonstrating better performance than the state-of-the-art tools DNorm and TaggerOne in recognizing MeSH terms from short biomedical phrases. The semantic similarity in MeSH terms recognized by pyMeSHSim and the previous manual work was calculated by pyMeSHSim and another semantic analysis tool meshes, respectively. The result indicated that the correlation of semantic similarity analysed by two tools reached as high as 0.89-0.99. CONCLUSIONS: The integrative MeSH tool pyMeSHSim embedded with the MeSH MHs and SCRs realized the bio-NE recognition, normalization, and comparison in biomedical text-mining.


Assuntos
Medical Subject Headings , Semântica , Unified Medical Language System/normas , Humanos
14.
J Med Internet Res ; 22(4): e13369, 2020 04 13.
Artigo em Inglês | MEDLINE | ID: mdl-32281938

RESUMO

BACKGROUND: Despite increasing opportunities for acquiring health information online, discussion of the specific words used in searches has been limited. OBJECTIVE: The aim of this study was to clarify the medical information gap between medical professionals and the general public in Japan through health information-seeking activities on the internet. METHODS: Search and posting data were analyzed from one of the most popular domestic search engines in Japan (Yahoo! JAPAN Search) and the most popular Japanese community question answering service (Yahoo! Chiebukuro). We compared the frequency of 100 clinical words appearing in the clinical case reports of medical professionals (clinical frequency) with their frequency in Yahoo! JAPAN Search (search frequency) logs and questions posted to Yahoo! Chiebukuro (question frequency). The Spearman correlation coefficient was used to quantify association patterns among the three information sources. Additionally, user information (gender and age) in the search frequency associated with each registered user was extracted. RESULTS: Significant correlations were observed between clinical and search frequencies (r=0.29, P=.003), clinical and question frequencies (r=0.34, P=.001), and search and question frequencies (r=0.57, P<.001). Low-frequency words in clinical frequency (eg, "hypothyroidism," "ulcerative colitis") highly ranked in search frequency. Similarly, "pain," "slight fever," and "numbness" were highly ranked only in question frequency. The weighted average of ages was 34.5 (SD 2.7) years, and the weighted average of gender (man -1, woman +1) was 0.1 (SD 0.1) in search frequency. Some words were specifically extracted from the search frequency of certain age groups, including "abdominal pain" (10-20 years), "plasma cells" and "inflammatory findings" (20-30 years), "DM" (diabetes mellitus; 30-40 years), "abnormal shadow" and "inflammatory findings" (40-50 years), "hypertension" and "abnormal shadow" (50-60 years), and "lung cancer" and "gastric cancer" (60-70 years). CONCLUSIONS: Search and question frequencies showed similar tendencies, whereas search and clinical frequencies showed discrepancy. Low-clinical frequency words related to diseases such as "hypothyroidism" and "ulcerative colitis" had high search frequencies, whereas those related to symptoms such as "pain," "slight fever," and "numbness" had high question frequencies. Moreover, high search frequency words included designated intractable diseases such as "ulcerative colitis," which has an incidence of less than 0.1% in the Japanese population. Therefore, it is generally worthwhile to pay attention not only to major diseases but also to minor diseases that users frequently seek information on, and more words will need to be analyzed in the future. Some characteristic words for certain age groups were observed (eg, 20-40 years: "cancer"; 40-60 years: diagnoses and diseases identified in health examinations; 60-70 years: diseases with late adulthood onset and "death"). Overall, this analysis demonstrates that medical professionals as information providers should be aware of clinical frequency, and medical information gaps between professionals and the general public should be bridged.


Assuntos
Serviços de Atendimento/normas , Medical Subject Headings/estatística & dados numéricos , Ferramenta de Busca/métodos , Adolescente , Adulto , Criança , Feminino , Humanos , Internet , Japão , Masculino , Pessoa de Meia-Idade , Inquéritos e Questionários , Adulto Jovem
15.
Klin Monbl Augenheilkd ; 237(4): 527-530, 2020 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-32330978

RESUMO

PURPOSE: To assess the prevalence of epiphora in the general adult population based on PubMed search citations. METHODS: 1) Electronic PubMed MEDLINE database search (September 13, 2019) with the terms (Medical Subject Headings or MeSH) "prevalence" and "epiphora", 2) "epiphora" and "dry eye", and 3) "prevalence", "epiphora", and "dry eye". Review of all citations from these searches containing the term "epiphora" either in their abstract or title. RESULTS: 1) PubMed search retrieved 2 617 137 citations for "prevalence", 26 135 for "epiphora", and 2554 for "prevalence" AND "epiphora". Within the latter 2554 citations, the word "epiphora" appeared in the abstract or title of only 109 citations (< 5%). None of these 109 citations assessed the prevalence of epiphora in the adult general population as the primary end point. Only one abstract mentioned that out of 125 patients, 7.2% indicated, retrospectively, that they had already had epiphora before cataract surgery. Two large population-based studies addressed the incidence of epiphora, not in adults, but in infants (20%) and children (7.7%). 2) The PubMed search showed 22 487 citations for "dry eye", 30 211 for "epiphora" OR "dry eye", and up to 18 414 joint citations for the terms "epiphora" AND "dry eye". These 18 414 citations were 70 and 82% of the number of MeSH citations for "epiphora" and "dry eye" alone, respectively. Of these 18 414 citations, the word "epiphora" only appeared in 131 citations (< 1%), one of them being an extra report mentioning a 32% incidence of epiphora among postmenopausal women. 3) The search found 2206 citations for "prevalence" AND "epiphora" AND "dry eye", with only 10 of them (< 1%) containing the word "epiphora". CONCLUSIONS: Despite a large number of citations retrieved by PubMed searches, there seems to be a lack of studies on the prevalence of epiphora in the adult general population. There is apparently also a large number of overlapping PubMed citations retrieved for searches with the terms "epiphora" AND "dry eye", although more than 99% of them did not even display the word "epiphora". Although epiphora is considered a common complain, its prevalence in the adult general population deserves to be further assessed.


Assuntos
Medical Subject Headings , Adulto , Criança , Feminino , Humanos , MEDLINE , Prevalência , PubMed , Estudos Retrospectivos
16.
Biomed Khim ; 66(1): 7-17, 2020 Jan.
Artigo em Russo | MEDLINE | ID: mdl-32116222

RESUMO

This paper proposes a method of comparative analysis of scientific trajectories based on bibliographic profiles. The bibliographic profile ("meshprint") is a list of MeSH terms (key terms used to index articles in the PubMed), indicating the relative frequency of occurrence of each term in the scientist's articles. Comparison of personalized bibliographic profiles can be represented in the form of a semantic network, where the nodes are the names of scientists, and the relationships are proportional to the calculated measures of similarity of bibliographic profiles. The proposed method was used to analyze the semantic network of scientists united by the academic school of the academician A.I. Archakov. The results of the work allowed us to show the relationship between the scientific trajectories of one scientific school and to correlate the results with world trends.


Assuntos
Algoritmos , Bibliometria , Medical Subject Headings , Editoração/tendências , PubMed
17.
Int J Med Inform ; 137: 104101, 2020 05.
Artigo em Inglês | MEDLINE | ID: mdl-32088556

RESUMO

OBJECTIVE: To develop an algorithm for identifying acronym 'sense' from clinical notes without requiring a clinically annotated training set. MATERIALS AND METHODS: Our algorithm is called CLASSE GATOR: Clinical Acronym SenSE disambiGuATOR. CLASSE GATOR extracts acronyms and definitions from PubMed Central (PMC). A logistic regression model is trained using words associated with specific acronym-definition pairs from PMC. CLASSE GATOR uses this library of acronym-definitions and their corresponding word feature vectors to predict the acronym 'sense' from Beth Israel Deaconess (MIMIC-III) neonatal notes. RESULTS: We identified 1,257 acronyms and 8,287 definitions including a random definition from 31,764 PMC articles on prenatal exposures and 2,227,674 PMC open access articles. The average number of senses (definitions) per acronym was 6.6 (min = 2, max = 50). The average internal 5-fold cross validation was 87.9 % (on PMC). We found 727 unique acronyms (57.29 %) from PMC were present in 105,044 neonatal notes (MIMIC-III). We evaluated the performance of acronym prediction using 245 manually annotated clinical notes with 9 distinct acronyms. CLASSE GATOR achieved an overall accuracy of 63.04 % and outperformed random for 8/9 acronyms (88.89 %) when applied to clinical notes. We also compared our algorithm with UMN's acronym set, and found that CLASSE GATOR outperformed random for 63.46 % of 52 acronyms when using logistic regression, 75.00 % when using Bert and 76.92 % when using BioBert as the prediction algorithm within CLASSE GATOR. CONCLUSIONS: CLASSE GATOR is the first automated acronym sense disambiguation method for clinical notes. Importantly, CLASSE GATOR does not require an expensive manually annotated acronym-definition corpus for training.


Assuntos
Abreviaturas como Assunto , Algoritmos , Registros Eletrônicos de Saúde/estatística & dados numéricos , Medical Subject Headings/estatística & dados numéricos , Processamento de Linguagem Natural , Reconhecimento Automatizado de Padrão , Humanos , Recém-Nascido
18.
J Cancer Res Clin Oncol ; 146(4): 985-1001, 2020 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-31955287

RESUMO

PURPOSE: Oral mucositis is a common, painful side effect of cancer treatment-be it locoregional (e.g. irradiation) or systemic (e. g. chemotherapy). Phytotherapy is often used by patients to alleviate symptoms. However, knowledge on which medical plants are recommended by literature about Traditional European Medicine (TEM), their effect(s) on symptoms and their efficacy is severely lacking. Therefore, we developed a novel approach to assess traditional knowledge of herbals used in TEM and searched the online databases for studies reporting effects of these plants. METHODS: At first, online research did not yield a satisfying number of studies (MESH terms: "mucositis" OR "stomatitis" AND "herbal" OR "herbal medicine"). Trials were labelled by the country conducting the study. In parallel, we compiled a list of 78 plants recommended for treating oral mucositis by screening 14 books on TEM. Then, a "hit list" of the plants most often mentioned was composed and used further for a second online investigation using the Latin plant designations as MESH term. Studies of both online searches were pooled for analysis. RESULTS: There is a gap between traditional knowledge and trials investigating medical plants used by TEM. Overall, herbal remedies alleviate oral mucositis and especially, gingivitis well. There is good evidence for using Matricaria recutita L., Salvia officinalis L., Calendula officinalis L. and Thymus spp. L. for treating oral mucositis. CONCLUSION: Clinical trials investigating medical plants known in TEM are rare. However, following our research strategy, we could extrapolate four plants with good evidence for alleviating symptoms of oral mucositis and gingivitis.


Assuntos
Fitoterapia/métodos , Plantas Medicinais , Estomatite/tratamento farmacológico , Antineoplásicos/efeitos adversos , Europa (Continente) , Humanos , Armazenamento e Recuperação da Informação , Medical Subject Headings , Neoplasias/tratamento farmacológico , Estomatite/induzido quimicamente , Revisões Sistemáticas como Assunto
19.
Bioinformatics ; 36(6): 1881-1888, 2020 03 01.
Artigo em Inglês | MEDLINE | ID: mdl-31738408

RESUMO

MOTIVATION: In light of the massive growth of the scientific literature, text mining is increasingly used to extract biological pathways. Though multiple tools explore individual connections between genes, diseases and drugs, few extensively synthesize pathways for specific diseases and drugs. RESULTS: Through community detection of a literature network, we extracted 3444 functional gene groups that represented biological pathways for specific diseases and drugs. The network linked Medical Subject Headings (MeSH) terms of genes, diseases and drugs that co-occurred in publications. The resulting communities detected highly associated genes, diseases and drugs. These significantly matched current knowledge of biological pathways and predicted future ones in time-stamped experiments. Likewise, disease- and drug-specific communities also recapitulated known pathways for those given diseases and drugs. Moreover, diseases sharing communities had high comorbidity with each other and drugs sharing communities had many common side effects, consistent with related mechanisms. Indeed, the communities robustly recovered mutual targets for drugs [area under Receiver Operating Characteristic curve (AUROC)=0.75] and shared pathogenic genes for diseases (AUROC=0.82). These data show that literature communities inform not only just known biological processes but also suggest novel disease- and drug-specific mechanisms that may guide disease gene discovery and drug repurposing. AVAILABILITY AND IMPLEMENTATION: Application tools are available at http://meteor.lichtargelab.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Medical Subject Headings , Publicações , Mineração de Dados , Reposicionamento de Medicamentos
20.
Bioinformatics ; 36(5): 1533-1541, 2020 03 01.
Artigo em Inglês | MEDLINE | ID: mdl-31596475

RESUMO

MOTIVATION: With the rapidly growing biomedical literature, automatically indexing biomedical articles by Medical Subject Heading (MeSH), namely MeSH indexing, has become increasingly important for facilitating hypothesis generation and knowledge discovery. Over the past years, many large-scale MeSH indexing approaches have been proposed, such as Medical Text Indexer, MeSHLabeler, DeepMeSH and MeSHProbeNet. However, the performance of these methods is hampered by using limited information, i.e. only the title and abstract of biomedical articles. RESULTS: We propose FullMeSH, a large-scale MeSH indexing method taking advantage of the recent increase in the availability of full text articles. Compared to DeepMeSH and other state-of-the-art methods, FullMeSH has three novelties: (i) Instead of using a full text as a whole, FullMeSH segments it into several sections with their normalized titles in order to distinguish their contributions to the overall performance. (ii) FullMeSH integrates the evidence from different sections in a 'learning to rank' framework by combining the sparse and deep semantic representations. (iii) FullMeSH trains an Attention-based Convolutional Neural Network for each section, which achieves better performance on infrequent MeSH headings. FullMeSH has been developed and empirically trained on the entire set of 1.4 million full-text articles in the PubMed Central Open Access subset. It achieved a Micro F-measure of 66.76% on a test set of 10 000 articles, which was 3.3% and 6.4% higher than DeepMeSH and MeSHLabeler, respectively. Furthermore, FullMeSH demonstrated an average improvement of 4.7% over DeepMeSH for indexing Check Tags, a set of most frequently indexed MeSH headings. AVAILABILITY AND IMPLEMENTATION: The software is available upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Indexação e Redação de Resumos , Medical Subject Headings , MEDLINE , PubMed , Semântica , Software
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