year 15, Issue 5 (September - October 2021)                   Iran J Med Microbiol 2021, 15(5): 508-536 | Back to browse issues page


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Moosavi S S, Farshid R, Jafari Baghi Abadi S. The Role of Medical and Health Archives in Scientific Research From a Scientometrics Perspective. Iran J Med Microbiol 2021; 15 (5) :508-536
URL: http://ijmm.ir/article-1-1341-en.html
1- Department of Information Science, Faculty of Management, University of Tehran, Tehran, Iran
2- Department of Information Science, Faculty of Psychology and Educational Sciences, Kharazmi University, Tehran, Iran
3- Department of Information Science, Faculty of Management, University of Tehran, Tehran, Iran , jafari.somayyeh@gmail.com
Abstract:   (2638 Views)

Background and Objective: This study aimed to investigate the role and application of medical and health archives in scientific research based on research topics indexed in WOS.
Materials and Methods: This study was a descriptive study with a scientometrics approach and the method of co-word analysis and hierarchical clustering and Strategic graph. The research population was scientific products related to medical and health archives. Data analysis was performed by HistCite, Bibexecl, UCINET, Excel and SPSS softwares.
Results: In the last three decades, 323 related studies were retrieved. The research dissemination has been increasing with an average annual growth rate of 34.87%. The United States and Univ Pittsburgh were ranked first in terms of publishing and citation rates. Iran ranks 14th with 7 researches. The field of GENERAL INTERNAL MEDICINE has had the largest share of 41 research among 88 participating research areas. The mortality keyword has the most frequency. Co-word clustering in medical archival research led to the formation of 7 clusters. Findings from the calculation of the density and centrality scores showed that "Cluster 3: smart Medicine" has the highest centrality and "Cluster 6: Open Data" have the highest density score.
Conclusion: Medical archives play an important role in discovering the causes of mortality and subsequently reducing mortality, preventing diseases and their risks and prevalence, improving diagnoses, treatments, and ultimately improving the health system.

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Type of Study: Original Research Article | Subject: Deep learning
Received: 2021/05/21 | Accepted: 2021/08/4 | ePublished: 2021/09/5

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