A thematic study of creativity research in Spain within the social sciences and psychology / Un estudio temático de la investigación en creatividad en España en las ciencias sociales y psicología

La creatividad se está convirtiendo en una habilidad necesaria en un mundo donde los robots superan cada vez más a las personas en las rutinas diarias. Para desarrollar eficientemente el campo de investigación de la creatividad, los académicos necesitan saber dónde están. Este artículo utiliza un enfoque bibliométrico para estudiar temas y características de la investigación en creatividad en España. Los resultados indican que la producción ha ido creciendo durante las últimas décadas. En comparación con la psicología, la creatividad en las ciencias sociales parece ser un área poco citada, local y endogámica. Para las ciencias sociales, los temas motores en la última década fueron a) la creatividad en niños y estudiantes en un entorno educativo, b) la innovación y creación de conocimiento en un entorno laboral, y c) las ciudades creativas. Los temas motores en psicología han sido a) las características individuales para generar conocimientos (por ejemplo, habilidades, improvisación, funciones ejecutivas) y b) la inteligencia emocional. Sugerimos algunos temas para futuras investigaciones, como la colaboración creativa en un entorno virtual, la co-creación de valor, y cómo las máquinas pueden ayudar a los humanos a impulsar su creatividad.(AU)

Creativity is becoming one necessary human skill in a world where robots increasingly outperform people in daily routines. In order to efficiently develop creativity as a research field, scholars need to know where they are.We employed a bibliometric approach to study themes and characteristics of creativity research in Spain.The results indicated that publication production in the field has been growing during the last dec-ades. Compared to psychology, creativity in the social sciences seemed to be an undercited, local,and endogamic area. For social sciences, motor themes in the last decade were a) creativity in children and students in the educational environment, b) innovation and knowledge creation in a work-ing environment, and c) cities and creativity. The motor themes in psy-chology were a) individual characteristics for generating insights (e.g., skills, improvisation, executive functions) and b) emotional intelligence. We sug-gest some themes for future research, such as creative collaboration in vir-tual environments, value co-creation, and how machines can help humans boost their creativity.(AU)

Automated indexing using NLM's Medical Text Indexer (MTI) compared to human indexing in Medline: a pilot study.

Objective: In 2002, the National Library of Medicine (NLM) introduced semi-automated indexing of Medline using the Medical Text Indexer (MTI). In 2021, NLM announced that it would fully automate its indexing in Medline with an improved MTI by mid-2022. This pilot study examines indexing using a sample of records in Medline from 2000, and how an early, public version of MTI's outputs compares to records created by human indexers. Methods: This pilot study examines twenty Medline records from 2000, a year before the MTI was introduced as a MeSH term recommender. We identified twenty higher- and lower-impact biomedical journals based on Journal Impact Factor (JIF) and examined the indexing of papers by feeding their PubMed records into the Interactive MTI tool. Results: In the sample, we found key differences between automated and human-indexed Medline records: MTI assigned more terms and used them more accurately for citations in the higher JIF group, and MTI tended to rank the Male check tag more highly than the Female check tag and to omit Aged check tags. Sometimes MTI chose more specific terms than human indexers but was inconsistent in applying specificity principles. Conclusion: NLM's transition to fully automated indexing of the biomedical literature could introduce or perpetuate inconsistencies and biases in Medline. Librarians and searchers should assess changes to index terms, and their impact on PubMed's mapping features for a range of topics. Future research should evaluate automated indexing as it pertains to finding clinical information effectively, and in performing systematic searches.

Mapping Research Trends of Medications for Multidrug-Resistant Pulmonary Tuberculosis Based on the Co-Occurrence of Specific Semantic Types in the MeSH Tree: A Bibliometric and Visualization-Based Analysis of PubMed Literature (1966-2020).

Background: Before the COVID-19 pandemic, tuberculosis is the leading cause of death from a single infectious agent worldwide for the past 30 years. Progress in the control of tuberculosis has been undermined by the emergence of multidrug-resistant tuberculosis. The aim of the study is to reveal the trends of research on medications for multidrug-resistant pulmonary tuberculosis (MDR-PTB) through a novel method of bibliometrics that co-occurs specific semantic Medical Subject Headings (MeSH). Methods: PubMed was used to identify the original publications related to medications for MDR-PTB. An R package for text mining of PubMed, pubMR, was adopted to extract data and construct the co-occurrence matrix-specific semantic types. Biclustering analysis of high-frequency MeSH term co-occurrence matrix was performed by gCLUTO. Scientific knowledge maps were constructed by VOSviewer to create overlay visualization and density visualization. Burst detection was performed by CiteSpace to identify the future research hotspots. Results: Two hundred and eight substances (chemical, drug, protein) and 147 diseases related to MDR-PTB were extracted to form a specific semantic co-occurrence matrix. MeSH terms with frequency greater than or equal to six were selected to construct high-frequency co-occurrence matrix (42 × 20) of specific semantic types contains 42 substances and 20 diseases. Biclustering analysis divided the medications for MDR-PTB into five clusters and reflected the characteristics of drug composition. The overlay map indicated the average age gradients of 42 high-frequency drugs. Fifteen top keywords and 37 top terms with the strongest citation bursts were detected. Conclusion: This study evaluated the literatures related to MDR-PTB drug therapy, providing a co-occurrence matrix model based on the specific semantic types and a new attempt for text knowledge mining. Compared with the macro knowledge structure or hot spot analysis, this method may have a wider scope of application and a more in-depth degree of analysis.

The 10 top-cited authors who published papers in journal medicine since 2000 using the betweenness centrality to identify unique names: Bibliometric analysis.

BACKGROUND: Numerous studies have explored the most productive and influential authors in a specific field. However, 2 challenges arise when conducting such research. First, some authors may have identical names in the study data, and second, the contributions of coauthors may vary in the article by line, requiring consideration. Failure to address these issues may result in biased research findings. Our objective was to illustrate how the author-weighted scheme (AWS) and betweenness centrality (BC) can be employed to identify the 10 most frequently cited authors in a particular journal and analyze their research themes. METHODS: We collected 24,058 abstracts from the PubMed library between 2000 and 2020 using the keyword "Medicine [Journal]." Author names, countries/regions, and medical subject headings (MeSH terms) were collected. The AWS to identify the top 10 authors with a higher x-index was applied. To address the issue of authors with identical names affiliated with different research institutes, we utilized the BC method. Social network analysis (SNA) was conducted, and 10 major clusters were identified to highlight authors with a higher x-index within the corresponding clusters. We utilized SNA to analyze the MeSH terms from articles of the 10 top-cited authors to identify their research themes. RESULTS: Our findings revealed the following: within the top 10 cited authors, 2 authors from China shared identical names with Jing Li and Tao-Wang; JA Winkelstein from Maryland (US) had the highest x-index (15.58); Chia-Hung Kao from Taiwan was the most prolific author, having published 115 articles in Medicine since 2003; and the 3 primary research themes, namely, complications, etiology, and epidemiology, were identified using MeSH terms from the 10 most frequently cited authors. CONCLUSIONS: Using AWS and BC, we identified the top 10 most cited authors. The research methods we utilized in this study (BC and AWS) have the potential to be applied to other bibliometric analyses in the future.

Characterizing terminology applied by authors and database producers to informatics literature on consumer engagement with wearable devices.

OBJECTIVE: Identifying consumer health informatics (CHI) literature is challenging. To recommend strategies to improve discoverability, we aimed to characterize controlled vocabulary and author terminology applied to a subset of CHI literature on wearable technologies. MATERIALS AND METHODS: To retrieve articles from PubMed that addressed patient/consumer engagement with wearables, we developed a search strategy of textwords and Medical Subject Headings (MeSH). To refine our methodology, we used a random sample of 200 articles from 2016 to 2018. A descriptive analysis of articles (N = 2522) from 2019 identified 308 (12.2%) CHI-related articles, for which we characterized their assigned terminology. We visualized the 100 most frequent terms assigned to the articles from MeSH, author keywords, CINAHL, and Engineering Databases (Compendex and Inspec together). We assessed the overlap of CHI terms among sources and evaluated terms related to consumer engagement. RESULTS: The 308 articles were published in 181 journals, more in health journals (82%) than informatics (11%). Only 44% were indexed with the MeSH term "wearable electronic devices." Author keywords were common (91%) but rarely represented consumer engagement with device data, eg, self-monitoring (n = 12, 0.7%) or self-management (n = 9, 0.5%). Only 10 articles (3%) had terminology from all sources (authors, PubMed, CINAHL, Compendex, and Inspec). DISCUSSION: Our main finding was that consumer engagement was not well represented in health and engineering database thesauri. CONCLUSIONS: Authors of CHI studies should indicate consumer/patient engagement and the specific technology investigated in titles, abstracts, and author keywords to facilitate discovery by readers and expand vocabularies and indexing.

Developing a Comprehensive Search Strategy for the Systematic Review of Clinical Decision Support Systems for Nursing Practice.

The search strategy of a literature review is of utmost importance as it impacts the validity of its findings. In order to build the best query to guide the literature search on clinical decision support systems applied to nursing clinical practice, we developed an iterative process capitalizing on previous systematic reviews published on similar topics. Three reviews were analyzed relatively to their detection performance. Errors in the choice of keywords and terms used in title and abstract (missing MeSH terms, failure to use common terms), may make relevant articles invisible.

Diferença entre os vocábulos bucal e oral na terminologia odontológica e seus significados na língua portuguesa / Difference between oral and oral vocables in dental terminology and their meanings in portuguese language

O objetivo do presente trabalho foi discutir sobre a diferença entre os termos bucal e oral na Odontologia, tendo como respaldo a Língua Portuguesa. A metodologia bibliográfica buscou se ancorar em teóricos da linguagem e da Odontologia para investigar a diferença entre tais palavras. Após leitura de textos que versaram sobre essas vertentes, observamos que a principal diferença residiu na origem das palavras bucca e os, originárias do latim clássico e vulgar, respectivamente, com significados distintos. Todavia, ao migrarem para o português, os falantes escolheram o termo bucca em detrimento de os, o qual ainda hoje é usado ao lado de oral, com sentidos semelhantes. Notamos, ainda, que para os profissionais da saúde seria importante padronizar a terminologia, pois facilitaria a compreensão desses termos para pacientes e profissionais de outras áreas, tais como os tradutores; por outro lado, ficou nítido que, em alguns momentos, a unificação terminológica seria mais difícil, pois os contextos de uso teriam que ser mudados. Por fim, destacamos que estudar estes vocábulos no contexto da Odontologia é importante para que tanto pacientes quanto os profissionais de saúde, ou de áreas similares conheçam a peculiar diferença(AU)

The objective of the present work was to discuss the difference between the terms oral and oral in Dentistry, based on the Portuguese language. The bibliographic methodology sought to anchor in language and dentistry theorists to investigate the difference between such words. After reading texts that dealt with these aspects, we observed that the main difference resided in the origin of the word bucca and os, originating from classical and vulgar Latin, respectively, with different meanings. However, when migrating to Portuguese, the speakers chose the term bucca over os, which is still used alongside oral, with similar meanings. We also noted that for health professionals it would be important to standardize the terminology, as it would facilitate the understanding of these terms for patients and professionals from other areas, such as translators; on the other hand, it was clear that at times, terminological unification would be more difficult, as the contexts of use would have to be changed. Finally, we emphasize that studying these words in the context of Dentistry is important so that both patients and health professionals, or from similar areas, know the peculiar difference(AU)

WeakMeSH: Leveraging provenance information for weakly supervised classification of biomedical articles with emerging MeSH descriptors.

Medical Subject Headings (MeSH) is a hierarchically structured thesaurus created by the National Library of Medicine of USA. Each year the vocabulary gets revised, bringing forth different types of changes. Those of particular interest are the ones that introduce new descriptors in the vocabulary either brand new or those who come up as a product of a complex change. These new descriptors often lack ground truth articles and rendering learning models that require supervision not applicable. Furthermore, this problem is characterized by its multi label nature and the fine-grained character of the descriptors that play the role of classes, requiring expert supervision and a lot of human resources. In this work, we alleviate these issues through retrieving insights from provenance information about those descriptors present in MeSH to create a weakly labeled train set for them. At the same time, we make use of a similarity mechanism to further filter the weak labels obtained through the descriptor information mentioned earlier. Our method, called WeakMeSH, was applied on a large-scale subset of the BioASQ 2018 data set consisting of 900 thousand biomedical articles. The performance of our method was evaluated on BioASQ 2020 against several other approaches that had given competitive results in similar problems in the past, or apply alternative transformations against the proposed one, as well as some variants that showcase the importance of each different component of our proposed approach. Finally, an analysis was performed on the different MeSH descriptors each year to assess the applicability of our method on the thesaurus.

Principales tecnologías de indización en las ciencias de la salud que se emplean en Occidente / Main indexing healthcare technologies used in West

En la actualidad, las tecnologías de indización en las ciencias de la salud están aportando mu-chos beneficios para el ámbito biomédico y la estandarización de su correspondiente termino-logía, puesto que esta cuestión es fundamental para lograr un diagnóstico médico más preciso e inequívoco Por esta razón, en este artículo se ha explicado con detalle cómo funcionan estas tecnologías: Terminología Anatómica In-ternacional (TAI), Medical Subject Headings y el Systematized Nomenclature of Medicine Cli-nical terminology (SNOMED CT), así como, las razones de la importancia de su uso para los sanitarios y los terminólogos.(AU)

Nowadays, healthcare indexing technologies are profiting the biomedical field and the stand-ardization of its corresponding terminology, since this is essential to achieve a more pre-cise and unequivocal medical diagnosis. Thus, in this article it has been performed a thorough explanation on how these healthcare technolo-gies work: International Anatomical Terminology (TAI), Medical Subject Headings and the Sys-tematised Nomenclature of Medicine Clinical terminology (SNOMED CT), as well as it was elucidated the reasons of its use for healthcare professionals and terminologists.(AU)

A comparison of 3 productive authors' research domains based on sources from articles, cited references and citing articles using social network analysis.

BACKGROUND: An individual's research domain (RD) can be determined from objective publication data (e.g., medical subject headings and Medical Subject Headings (MeSH) terms) by performing social network analysis. Bibliographic coupling (such as cocitation) is a similarity metric that relies on citation analysis to determine the similarity in RD between 2 articles. This study compared RD consistency between articles as well as their cited references and citing articles (ARCs). METHODS: A total of 1388 abstracts were downloaded from PubMed and authored by 3 productive authors. Based on the top 3 clusters in social network analysis, similarity in RD was observed by comparing their consistency using the major MeSH terms in author articles, cited references and citing articles (ARC). Impact beam plots with La indices were drawn and compared for each of the 3 authors. RESULTS: Sung-Ho Jang (South Korea), Chia-Hung Kao (Taiwan), and Chin-Hsiao Tseng (Taiwan) published 445, 780, and 163 articles, respectively. Dr Jang's RD is physiology, and Dr Kao and Dr Tseng's RDs are epidemiology. We confirmed the consistency of the RD terms by comparing the major MeSH terms in the ARC. Their La indexes were 5, 5, and 6, where a higher value indicates more extraordinary research achievement. CONCLUSION: RD consistency was confirmed by comparing the main MeSH terms in ARC. The 3 approaches of RD determination (based on author articles, the La index, and the impact beam plots) were recommended for bibliographical studies in the future.

Citation analysis of the 100 top-cited articles on the topic of hidradenitis suppurativa since 2013 using Sankey diagrams: Bibliometric analysis.

BACKGROUND: Hidradenitis suppurativa (HS) is a chronic, inflammatory and debilitating dermatosis characterized by painful nodules, sinus tracts and abscesses in apocrine gland-bearing areas that predominantly affect women worldwide. New therapeutic interventions based on the clinical manifestations of patients have recently been introduced in numerous articles. However, which countries, journals, subject categories, and articles have the ultimate influence remain unknown. This study aimed to display influential entities in 100 top-cited HS-related articles (T100HS) and investigate whether medical subject headings (i.e., MeSH terms) can be used to predict article citations. METHODS: T100HS data were extracted from PubMed since 2013. Subject categories were classified by MeSH terms using social network analysis. Sankey diagrams were applied to highlight the top 10 influential entities in T100HS from the three aspects of publication, citations, and the composited score using the hT index. The difference in article citations across subject categories and the predictive power of MeSH terms on article citations in T100HS were examined using one-way analysis of variance and regression analysis. RESULTS: The top three countries (the US, Italy, and Spain) accounts for 54% of the T100HS. The T100HS impact factor (IF) is 12.49 (IF = citations/100). Most articles were published in J Am Acad Dermatol (15%; IF = 18.07). Eight subject categories were used. The "methods" was the most frequent MeSH term, followed by "surgery" and "therapeutic use". Saunte et al, from Roskilde Hospital, Denmark, had 149 citations in PubMed for the most cited articles. Sankey diagrams were used to depict the network characteristics of the T100HS. Article citations did not differ by subject category (F(7, 92) = 1.97, P = .067). MeSH terms were evident in the number of article citations predicted (F(1, 98) = 129.1106; P < .001). CONCLUSION: We achieved a breakthrough by displaying the characteristics of the T100HS network on the Sankey diagrams. MeSH terms may be used to classify articles into subject categories and predict T100HS citations. Future studies can apply the Sankey diagram to the bibliometrics of the 100 most-cited articles.

Comparing the similarity and differences in MeSH terms associated with spine-specific journals using the forest plot: A bibliometric analysis.

BACKGROUND: A common concern in the literature is the comparison of the similarities and differences between research journals, as well as the types of research they publish. At present, there are no clear methodologies that can be applied to a given article of interest. When authors use an effective and efficient method to locate journals in similar fields, they benefit greatly. By using the forest plot and major medical subject headings (MeSH terms) of Spine (Phila Pa 1976) compared to Spine J, this study: displays relatively similar journals to the target journal online and identifies the effect of the similarity odds ratio of Spine (Phila Pa 1976) compared to Spine J. METHODS: From the PubMed library, we downloaded 1000 of the most recent top 20 most similar articles related to Spine (Phila Pa 1976) and then plotted the clusters of related journals using social network analysis (SNA). The forest plot was used to compare the differences in MeSH terms for 2 journals (Spine (Phila Pa 1976) and Spine J) based on odds ratios. The heterogeneity of the data was evaluated using the Q statistic and the I-square (I2) index. RESULTS: This study shows that: the journals related to Spine (Phila Pa 1976) can easily be presented on a dashboard via Google Maps; 8 journal clusters were identified using SNA; the 3 most frequently searched MeSH terms are surgery, diagnostic imaging, and methods; and the odds ratios of MeSH terms only show significant differences with the keyword "surgery" between Spine (Phila Pa 1976) and Spine J with homogeneity at I2 = 17.7% (P = .27). CONCLUSIONS: The SNA and forest plot provide a detailed overview of the inter-journal relationships and the target journal using MeSH terms. Based on the findings of this research, readers are provided with knowledge and concept diagrams that can be used in future submissions to related journals.

The Hirsch-index in self-citation rates with articles in Medicine (Baltimore): Bibliometric analysis of publications in two stages from 2018 to 2021.

BACKGROUND: The Hirsch-index (h-index) is a measure of academic productivity that incorporates both the quantity and quality of an author's output. However, it is still affected by self-citation behaviors. This study aims to determine the research output and self-citation rates (SCRs) in the Journal of Medicine (Baltimore), establishing a benchmark for bibliometrics, in addition to identifying significant differences between stages from 2018 to 2021. METHODS: We searched the PubMed database to obtain 17,912 articles published between 2018 and 2021 in Medicine (Baltimore). Two parts were carried out to conduct this study: the categories were clustered according to the medical subject headings (denoted by midical subject headings [MeSH] terms) using social network analysis; 3 visualizations were used (choropleth map, forest plot, and Sankey diagram) to identify dominant entities (e.g., years, countries, regions, institutes, authors, categories, and document types); 2-way analysis of variance (ANOVA) was performed to differentiate outputs between entities and stages, and the SCR with articles in Medicine (Baltimore) was examined. SCR, as well as the proportion of self-citation (SC) in the previous 2 years in comparison to SC were computed. RESULTS: We found that South Korea, Sichuan (China), and Beijing (China) accounted for the majority of articles in Medicine (Baltimore); ten categories were clustered and led by 3 MeSh terms: methods, drug therapy, and complications; and more articles (52%) were in the recent stage (2020-2021); no significant difference in counts was observed between the 2 stages based on the top ten entities using the forest plot (Z = 0.05, P = .962) and 2-way ANOVA (F = 0.09, P = .76); the SCR was 5.69% (<15%); the h-index did not differ between the 2 collections of self-citation inclusion and exclusion; and the SC in the previous 2 years accounted for 70% of the self-citation exclusion. CONCLUSION: By visualizing the characteristics of a given journal, a breakthrough was made. Subject categories can be classified using MeSH terms. Future bibliographical studies are recommended to perform the 2-way ANOVA and then compare the outputs from 2 stages as well as the changes in h-indexes between 2 sets of self-citation inclusion and exclusion.

Semisupervised neural biomedical sense disambiguation approach for aspect-based sentiment analysis on social networks.

Patient narratives on social networks contain large amounts of objective information, such as the descriptions of examinations and interventions. Sentiment analysis (SA) models are mostly used to evaluate the conveyed sentiments by patients in these narratives to assess positive or negative clinical outcomes or to judge the impact of a drug or a medical condition. To date, many state-of-the-art SA models often result in false assessment coverage due to the natural medical entities recognition deficiency and ambiguity problem. In this work, we propose a semisupervised-based neural sense disambiguation approach that helps to substantially define ambiguities, their levels, and the relational mappings between biomedical targets and dependencies for accurate aspect-based sentiment prediction. Three main modules are proposed: (1) generate a sentiment value based on extracted concepts and their synsets, (2) encode the representations of the contextual senses and sentiment inputs, and (3) estimate an aspect-based sentiment weight based on the context-dependency sentiment units vs. the biomedical sense. Both intrinsic and extrinsic evaluation proved how the proposed method have succeeded in pruning contextual sense feature generation and showed a strong agreement for biomedical data property parameterization and ambiguity type extraction. Thus, the model offers a significant rate of discrimination of biomedical natural concept senses by critically analysing constraints from conjunctions of positive or negative contextual semantics. A total of 21% of the vocabulary is drug names, 11% is a multiword drug reaction expressions, 7% is disease symptoms, and 5% is disease-related concepts such as symptoms and related therapy terms. Furthermore, the experiments on a multisource data from Twitter and health-related forums have overshadowed sentiment assessment and achieved an accuracy of 0.91 regarding concepts-based biomedical aspects. These results provide fresh insights into how to investigate biomedical knowledge, e.g., Medical Subject Headings (MeSH) and PubMed, to clarify the correspondence of various biomedical descriptive entities, definitions, and data properties from shared medication-related content.

Trend and prediction of citations on the topic of neuromuscular junctions in 100 top-cited articles since 2001 using a temporal bar graph: A bibliometric analysis.

BACKGROUND: A neuromuscular junction (NMJ) (or myoneural junction) is a chemical synapse between a motor neuron (MN) and a muscle fiber. Although numerous articles have been published, no such analyses on trend or prediction of citations in NMJ were characterized using the temporal bar graph (TBG). This study is to identify the most dominant entities in the 100 top-cited articles in NMJ (T100MNJ for short) since 2001; to verify the improved TBG that is viable for trend analysis; and to investigate whether medical subject headings (MeSH terms) can be used to predict article citations. METHODS: We downloaded T100MNJ from the PubMed database by searching the string ("NMJ" [MeSH Major Topic] AND ("2001" [Date - Modification]: "2021" [Date - Modification])) and matching citations to each article. Cluster analysis of citations was performed to select the most cited entities (e.g., authors, research institutes, affiliated countries, journals, and MeSH terms) in T100MNJ using social network analysis. The trend analysis was displayed using TBG with two major features of burst spot and trend development. Next, we examined the MeSH prediction effect on article citations using its correlation coefficients (CC) when the mean citations in MeSH terms were collected in 100 top-cited articles related to NMJ (T100NMJs). RESULTS: The most dominant entities (i.e., country, journal, MesH term, and article in T100NMJ) in citations were the US (with impact factor [IF] = 142.2 = 10237/72), neuron (with IF = 151.3 = 3630/24), metabolism (with IF = 133.02), and article authored by Wagh et al from Germany in 2006 (with 342 citing articles). The improved TBG was demonstrated to highlight the citation evolution using burst spots, trend development, and line-chart plots. MeSH terms were evident in the prediction power on the number of article citations (CC = 0.40, t = 4.34). CONCLUSION: Two major breakthroughs were made by developing the improved TBG applied to bibliographical studies and the prediction of article citations using the impact factor of MeSH terms in T100NMJ. These visualizations of improved TBG and scatter plots in trend, and prediction analyses are recommended for future academic pursuits and applications in other disciplines.

Ontology-based categorization of clinical studies by their conditions.

OBJECTIVE: The free-text Condition data field in the ClinicalTrials.gov is not amenable to computational processes for retrieving, aggregating and visualizing clinical studies by condition categories. This paper contributes a method for automated ontology-based categorization of clinical studies by their conditions. MATERIALS AND METHODS: Our method first maps text entries in ClinicalTrials.gov's Condition field to standard condition concepts in the OMOP Common Data Model by using SNOMED CT as a reference ontology and using Usagi for concept normalization, followed by hierarchical traversal of the SNOMED ontology for concept expansion, ontology-driven condition categorization, and visualization. We compared the accuracy of this method to that of the MeSH-based method. RESULTS: We reviewed the 4,506 studies on Vivli.org categorized by our method. Condition terms of 4,501 (99.89%) studies were successfully mapped to SNOMED CT concepts, and with a minimum concept mapping score threshold, 4,428 (98.27%) studies were categorized into 31 predefined categories. When validating with manual categorization results on a random sample of 300 studies, our method achieved an estimated categorization accuracy of 95.7%, while the MeSH-based method had an accuracy of 85.0%. CONCLUSION: We showed that categorizing clinical studies using their Condition terms with referencing to SNOMED CT achieved a better accuracy and coverage than using MeSH terms. The proposed ontology-driven condition categorization was useful to create accurate clinical study categorization that enables clinical researchers to aggregate evidence from a large number of clinical studies.

Predicting the number of citations of polycystic kidney disease with 100 top-cited articles since 2010: Bibliometric analysis.

BACKGROUND: Polycystic kidney disease (PKD) is a genetic disorder in which the renal tubules become structurally abnormal, resulting in the development and growth of multiple cysts within the kidneys. Numerous studies on PKD have been published in the literature. However, no such articles used medical subject headings (MeSH terms) to predict the number of article citations. This study aimed to predict the number of article citations using 100 top-cited PKD articles (T100PKDs) and dissect the characteristics of influential authors and affiliated counties since 2010. METHODS: We searched the PubMed Central® (PMC) database and downloaded 100PKDs from 2010. Citation analysis was performed to compare the dominant countries and authors using social network analysis (SNA). MeSh terms were analyzed by referring to their citations in articles and used to predict the number of article citations using its correlation coefficients (CC) to examine the prediction effect. RESULTS: We observed that the top 3 countries and journals in 100PKDs were the US (65%), Netherlands (7%), France (5%), J Am Soc Nephrol (21%), Clin J Am Soc Nephrol (8%), and N Engl J Med (6%); the most cited article (PMID = 23121377 with 473 citations) was authored by Vicente Torres from the US in 2012; and the most influential MeSH terms were drug therapy (3087.2), genetics (2997.83), and therapeutic use (2760.7). MeSH terms were evident in the prediction power of the number of article citations (CC = 0.37; t = 3.92; P < .01, n = 100). CONCLUSIONS: A breakthrough was made by developing a method using MeSH terms to predict the number of article citations based on 100PKDs. MeSH terms are evident in predicting article citations that can be applied to future research, not limited to PKD, as we did in this study.

Literature-based drug-drug similarity for drug repurposing: impact of Medical Subject Headings term refinement and hierarchical clustering.

Background: We describe herein, an improved procedure for drug repurposing based on refined Medical Subject Headings (MeSH) terms and hierarchical clustering method. Materials & methods: In the present study, we have employed MeSH data from MEDLINE (2019), 1669 US FDA approved drugs from Open FDA and a refined set of MeSH terms. Refinement of MeSH terms was performed to include terms related to mechanistic information of drugs and diseases. Results and Conclusions: In-depth analysis of the results obtained, demonstrated greater efficiency of the proposed approach, based on refined MeSH terms and hierarchical clustering, in terms of number of selected drug candidates for repurposing. Further, analysis of misclustering and size of noise clusters suggest that the proposed approach is reliable and can be employed in drug repurposing.

Using the forest plot to compare citation achievements in bibliographic and meta-analysis studies since 2011 using data on PubMed Central: A retrospective study.

BACKGROUND: We saw a steady increase in the number of bibliographic studies published over the years. The reason for this rise is attributed to the better accessibility of bibliographic data and software packages that specialize in bibliographic analyses. Any difference in citation achievements between bibliographic and meta-analysis studies observed so far need to be verified. In this study, we aimed to identify the frequently observed MeSH terms in these 2 types of study and investigate whether the highlighted MeSH terms are strongly associated with one of the study types. METHODS: By searching the PubMed Central database, 5121 articles relevant to bibliometric and meta-analysis studies were downloaded since 2011. Social network analysis was applied to highlight the major MeSH terms of quantitative and statistical methods in these 2 types of studies. MeSH terms were then individually tested for any differences in event counts over the years between study types using odds of 95% confidence intervals for comparison. RESULTS: In these 2 studies, we found that the most productive countries were the United States (19.9%), followed by the United Kingdom (8.8%) and China (8.7%); the most number of articles were published in PLoS One (2.9%), Stat Med (2.5%), and Res Synth (2.4%); and the most frequently observed MeSH terms were statistics and numerical data in bibliographic studies and methods in meta-analysis. Differences were found when compared to the event counts and the citation achievements in these 2 study types. CONCLUSION: The breakthrough was made by developing a dashboard using forest plots to display the difference in event counts. The visualization of the observed MeSH terms could be replicated for future academic pursuits and applications in other disciplines using the odds of 95% confidence intervals.

Usability Evaluation of a Modern Multilingual MeSH Browser.

Medical Subject Headings (MeSH) is one of the most important vocabularies for information retrieval in medical research. It enables fast and reliable retrieval of research on PubMed/MEDLINE, the world's largest body of medical literature. The original English version of the thesaurus can be accessed via a MeSH Browser developed by the NLM. Recently, a multilingual MeSH Browser was proposed to enable usage across languages. To improve upon the original system, a new user interface (UI) was developed using contemporary web design frameworks in combination with principles from cognitive science. It aims to simplify access for medical professionals and increase overall usability. Evaluating such design improvements continually is necessary to quantify the possible positive impact for online systems in medical research. This study therefore directly compares the resulting system to the NLM Browser, using an established online questionnaire. Results show significant improvements in content and navigation as well as overall user satisfaction, while offering feedback for future improvements. This underlines the benefits of employing contemporary web design in terms of usability and user satisfaction.