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Introduction: Research on hepatocellular carcinoma (HCC) has grown significantly, and researchers cannot access the vast amount of literature. This study aimed to explore the research progress in studying HCC over the past 30 years using a machine learning-based bibliometric analysis and to suggest future research directions. Methods: Comprehensive research was conducted between 1991 and 2020 in the public version of the PubMed database using the MeSH term "hepatocellular carcinoma." The complete records of the collected results were downloaded in Extensible Markup Language format, and the metadata of each publication, such as the publication year, the type of research, the corresponding author's country, the title, the abstract, and the MeSH terms, were analyzed. We adopted a latent Dirichlet allocation topic modeling method on the Python platform to analyze the research topics of the scientific publications. Results: In the last 30 years, there has been significant and constant growth in the annual publications about HCC (annual percentage growth rate: 7.34%). Overall, 62,856 articles related to HCC from the past 30 years were searched and finally included in this study. Among the diagnosis-related terms, "Liver Cirrhosis" was the most studied. However, in the 2010s, "Biomarkers, Tumor" began to outpace "Liver Cirrhosis." Regarding the treatment-related MeSH terms, "Hepatectomy" was the most studied; however, recent studies related to "Antineoplastic Agents" showed a tendency to supersede hepatectomy. Regarding basic research, the study of "Cell Lines, Tumors,'' appeared after 2000 and has been the most studied among these terms. Conclusion: This was the first machine learning-based bibliometric study to analyze more than 60,000 publications about HCC over the past 30 years. Despite significant efforts in analyzing the literature on basic research, its connection with the clinical field is still lacking. Therefore, more efforts are needed to convert and apply basic research results to clinical treatment. Additionally, it was found that microRNAs have potential as diagnostic and therapeutic targets for HCC.
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To provide nurse-led interprofessional practices in a healthcare setting, carrying out effective research that identifies the trends and characteristics of interprofessional education is necessary. This study aimed to objectively ascertain trends in the field through text network analysis of different types of interprofessional education literature. Titles and thesis abstracts were examined for terms "interprofessional education" and "nursing" and were found in 3926 articles from 1970 to August 2018. Python and Gephi software were used to analyze the data and visualize the networks. Keyword ranking was based on the frequency, degree centrality, and betweenness centrality. The terms "interprofessional," "education," "student," "nursing," and "health" were ranked the highest. According to topic analysis, the methods, provided programs, and outcome measures differed according to the research field. These findings can help create nurse-led research and effective future directions for interprofessional education pathways and topic selection. This will emphasize the importance of expanding research on various education programs and accumulating evidence regarding the professional and interdisciplinary impact these programs have on undergraduate and graduate students.
Assuntos
Educação em Enfermagem , Enfermeiras e Enfermeiros , Estudantes de Enfermagem , Humanos , Educação Interprofissional , Relações InterprofissionaisRESUMO
This study aimed to understand the trends in research on the quality of life of returning to work (RTW) cancer survivors using text network analysis. Titles and abstracts of each article were examined to extract terms, including "cancer survivors", "return to work", and "quality of life", which were found in 219 articles published between 1990 and June 2020. Python and Gephi software were used to analyze the data and visualize the networks. Keyword ranking was based on the frequency, degree centrality, and betweenness centrality. The keywords commonly ranked at the top included "breast", "patients", "rehabilitation", "intervention", "treatment", and "employment". Clustering results by grouping nodes with high relevance in the network led to four clusters: "participants and method", "type of research and variables", "RTW and education in adolescent and young adult cancer survivors", and "rehabilitation program". This study provided a visualized overview of the research on cancer survivors' RTW and quality of life. These findings contribute to the understanding of the flow of the knowledge structure of the existing research and suggest directions for future research.