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A BIBLIOMETRIC ANALYSIS OF THE RELATIONSHIP BETWEEN DIABETES AND ARTIFICIAL INTELLIGENCE

Yıl 2022, Cilt: 85 Sayı: 2, 249 - 257, 24.03.2022
https://doi.org/10.26650/IUITFD.928111

Öz

Objective: The applications of artificial intelligence in the field of medicine are progressing at a remarkable speed. Within the scope of this study, it is aimed to make a detailed examination of the studies published on diabetes and artificial intelligence, to determine any trends in these studies over time, to examine which subjects have been researched more and to explain the global interest in the subject. Material and Methods: In this study, 2534 studies published between 1985 and 2020 in the field of diabetes and artificial intelligence were analyzed using the R programming language through bibliometric data obtained from the Scopus database. Correlation analysis, ANOVA and regression analysis were performed to determine the relationship between the number of articles and years. Results: According to the analysis results, the number of publications between 1985 and 2015 was 604 and over the last 5 years, the number of publications have tripled to 1930. It was found that the country with the highest number of publications with 358 publications and 10426 citations was the United States of America (USA). Moreover, in the analyzed studies, the most used keywords and the use of these words together was also examined and the top 10 source platforms where the studies were published most have been presented in the study. According to regression analysis, it can be predicted that the number of articles to be published for 2021 is 242. Conclusion: As a result of the analysis in this study, it was determined that artificial intelligence and diabetes applications have become one of the important global research topics in today’s golden age of artificial intelligence, and it was also determined that there is an urgent need for artificial intelligence supported scientific studies to prevent diabetes or diagnose diabetes in the early period.

Destekleyen Kurum

Scientific Research Projects Coordination Unit of Istanbul University.

Proje Numarası

37697

Kaynakça

  • 1. Coşansu G. 21. Yüzyılın Sağlık Krizi: Diyabet. Florence Nightingale J Nurs 2014;17(2):115-22.
  • 2. International Diabetes Federation. IDF Diabetes Atlas. Vol. 9th Edition. 2019 [cited 2021 May 11]. Available from: https://www.diabetesatlas.org/en/resources/
  • 3. Satman I, Yilmaz T, Sengül A, Salman S, Salman F, Uygur S, et al. Population-based study of diabetes and risk characteristics in Turkey: Results of the Turkish Diabetes Epidemiology Study (TURDEP). Diabetes Care 2002;25(9):1551-6. [CrossRef]
  • 4. Onat A, Çakır H, Karadeniz Y, Dönmez İ, Karagöz A, Yüksel M, et al. Turkish Adult Risk Factor survey 2013: rapid rise in the prevalence of diabetes. Turk Kardiyol Dernegi Arsivi- Arch Turk Soc Cardiol 2014;42(6):511-6. [CrossRef]
  • 5. Süleymanlar G, Utaş C, Arinsoy T, Ateş K, Altun B, Altiparmak MR, et al. A population-based survey of Chronic REnal Disease In Turkey-the CREDIT study. Nephrol Dial Transplant 2011;26(6):1862-71. [CrossRef]
  • 6. Satman I, Omer B, Tutuncu Y, Kalaca S, Gedik S, Dinccag N, et al. Twelve-year trends in the prevalence and risk factors of diabetes and prediabetes in Turkish adults. Eur J Epidemiol 2013;28(2):169-80. [CrossRef]
  • 7. Nabiyev N. Yapay zeka. 4th ed. Ankara; 2012.
  • 8. Buch V, Varughese G, Maruthappu M. Artificial intelligence in diabetes care. Diabet Med 2018;35(4):495-7. [CrossRef]
  • 9. Bulut M. Önsöz. In: Bulut M, Dilmen N, Bora E, Gezer M, Erol Ç, Türker Şener L, editors. Sağlıkta Yapay Zekâ. İstanbul, Turkey: Çağlayan Kitabevi; 2019.
  • 10. Fagherazzi G, Ravaud P. Digital diabetes: Perspectives for diabetes prevention, management and research. Diabetes Metab 2019;45(4):322-9. [CrossRef]
  • 11. Ellahham S. Artificial Intelligence: The Future for Diabetes Care. Am J Med 2020;133(8):895-900. [CrossRef]
  • 12. Huang Y, McCullagh P, Black N, Harper R. Feature selection and classification model construction on type 2 diabetic patients’ data. Artif Intell Med 2007;41(3):251-62. [CrossRef]
  • 13. Makino M, Yoshimoto R, Ono M, Itoko T, Katsuki T, Koseki A, et al. Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning. Sci Rep 2019;9(11862):1-9. [CrossRef]
  • 14. Özmen EP, Özcan T. Diagnosis of diabetes mellitus using artificial neural network and classification and regression tree optimized with genetic algorithm. J Forecast 2020;39(4):661-70. [CrossRef]
  • 15. Wong TY, Sabanayagam C. Strategies to tackle the global burden of diabetic retinopathy: from epidemiology to artificial intelligence. Ophthalmologica 2020;243(1):9-20. [CrossRef]
  • 16. Zhao M, Jiang Y. Great expectations and challenges of artificial intelligence in the screening of diabetic retinopathy. Eye 2020;34(3):418-9. [CrossRef]
  • 17. Contreras I, Vehi J. Artificial intelligence for diabetes management and decision support: literature review. J Med Internet Res 2018;20(5):e10775. [CrossRef]
  • 18. Koçoğlu F. “Endüstri 4.0” Konusu Üzerine R Programlama Dili İle Bibliyometrik Analiz. In: Başal, Handan Asude, Ulutürk Y, Öner NK, editors. Modern dönemde edebiyat, eğitim, iktisat ve mühendislik. Ankara: Berikan Yayınevi; 2018. p. 859-89.
  • 19. TÜBİTAK ULAKBİM, Cahit Arf Bilgi Merkezi. Bibliyometrik Analiz Sıkça Sorulan Sorular. [cited 2020 Aug 27]. Available from: https://cabim.ulakbim.gov.tr/bibliyometrik-analiz/ bibliyometrik-analiz-sikca-sorulan-sorular/
  • 20. Ellegaard O, Wallin JA. The bibliometric analysis of scholarly production: How great is the impact? Scientometrics 2015;105(3):1809-31. [CrossRef]
  • 21. Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. How to conduct a bibliometric analysis: An overview and guidelines. J Bus Res 2021;133:285-96. [CrossRef]
  • 22. Baas J, Schotten M, Plume A, Cote G, Karimi R. Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quant Sci Stud 2020;1(1):377-86. [CrossRef]
  • 23. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2018. Available from: https://www.R-project. org/
  • 24. RStudio Team. RStudio: Integrated Development Environment for R Boston, MA: RStudio, PBC; 2020. Available from: http://www.rstudio.com/
  • 25. Aria M, Cuccurullo C. bibliometrix: An R-tool for comprehensive science mapping analysis. J Informetr 2017;11(4):959-75. [CrossRef]
  • 26. Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, et al. Welcome to the Tidyverse. J Open Source Softw 2019;4(43):1686. [CrossRef]
  • 27. Elango B, Rajendran P. Authorship trends and collaboration pattern in the marine sciences literature : a scientometric study. Int J Inf Dissem Technol 2012;2(3):166-9.
  • 28. Koseoglu MA. Mapping the institutional collaboration network of strategic management research: 1980-2014. Scientometrics 2016;109(1):203-26. [CrossRef]
  • 29. Kaynak O. The golden age of Artificial Intelligence. Discov Artif Intell 2021;1(1). [CrossRef]
  • 30. Agartan TI. Politics of success stories in the path towards Universal Health Coverage: The case of Turkey. Dev Policy Rev 2021;39(2):283-302. [CrossRef]
  • 31. Buse C, Unluonen, K. Economic evaluation of health tourism in Turkey. Journal of Tourismology 2020;6(1):99-109. [CrossRef]

DİYABET VE YAPAY ZEKÂ İLİŞKİSİ ÜZERİNE BİR BİBLİYOMETRİK ANALİZ

Yıl 2022, Cilt: 85 Sayı: 2, 249 - 257, 24.03.2022
https://doi.org/10.26650/IUITFD.928111

Öz

Amaç: Yapay zekânın sağlık alanındaki uygulamaları dikkat çekici hızla ilerlemektedir. Bu çalışma kapsamında diyabet ve yapay zekâ konusunda yayınlanan çalışmaların detaylı bir incelemesinin yapılması, çalışmaların zaman içindeki eğiliminin belirlenmesi, hangi konularda daha fazla araştırma yapıldığının incelenmesi ve konuyla ilgili küresel ilginin açığa çıkarılması amaçlanmıştır. Gereç ve Yöntem: Bu çalışmada 1985-2020 yılları arasında diyabet ve yapay zekâ alanında yayınlanan 2534 çalışma Scopus veri tabanından elde edilen bibliyometrik verilerle R programlama dili kullanılarak analiz edilmiştir. Yıllara göre makale sayısı arasındaki ilişkiyi açığa çıkarmak için, korelasyon analizi, varyans analizi (ANOVA) ve regresyon analizi gerçekleştirilmiştir. Bulgular: Yapılan analiz sonuçlarına göre, 1985-2015 yılları arasında üretilen yayın sayısının 604 olduğu ve son 5 yıldaki yayın sayısının bu sayıyı üçe katlayarak 1930’e çıkardığı tespit edilmiştir. Bununla birlikte, 358 yayınla en çok yayın yapan ve 10426 atıfla en çok atıf alan ülkenin, Amerika Birleşik Devletleri olduğu saptanmıştır. Ayrıca, incelenen araştırmalarda en çok kullanılan anahtar kelimeler ve bu kelimelerin bir arada kullanımı ve çalışmaların en çok yayınlandığı ilk 10 kaynak platform da araştırmada sunulmuştur. Gerçekleştirilen regresyon analizine göre 2021 yılı için yayınlanabilecek makale sayısının 242 olarak tahmin edilmiştir. Sonuç: Sonuç olarak, yapay zekânın altın çağını yaşadığı günümüzde, yapay zekâ ve diyabet uygulamalarının, küresel bazda önemli araştırma konularından biri hâline geldiği saptanmış olup, diyabeti önlemek veya erken dönemde teşhis etmek için yapay zekâ destekli bilimsel çalışmalara yoğun bir ihtiyacın bulunduğu tespit edilmiştir.

Proje Numarası

37697

Kaynakça

  • 1. Coşansu G. 21. Yüzyılın Sağlık Krizi: Diyabet. Florence Nightingale J Nurs 2014;17(2):115-22.
  • 2. International Diabetes Federation. IDF Diabetes Atlas. Vol. 9th Edition. 2019 [cited 2021 May 11]. Available from: https://www.diabetesatlas.org/en/resources/
  • 3. Satman I, Yilmaz T, Sengül A, Salman S, Salman F, Uygur S, et al. Population-based study of diabetes and risk characteristics in Turkey: Results of the Turkish Diabetes Epidemiology Study (TURDEP). Diabetes Care 2002;25(9):1551-6. [CrossRef]
  • 4. Onat A, Çakır H, Karadeniz Y, Dönmez İ, Karagöz A, Yüksel M, et al. Turkish Adult Risk Factor survey 2013: rapid rise in the prevalence of diabetes. Turk Kardiyol Dernegi Arsivi- Arch Turk Soc Cardiol 2014;42(6):511-6. [CrossRef]
  • 5. Süleymanlar G, Utaş C, Arinsoy T, Ateş K, Altun B, Altiparmak MR, et al. A population-based survey of Chronic REnal Disease In Turkey-the CREDIT study. Nephrol Dial Transplant 2011;26(6):1862-71. [CrossRef]
  • 6. Satman I, Omer B, Tutuncu Y, Kalaca S, Gedik S, Dinccag N, et al. Twelve-year trends in the prevalence and risk factors of diabetes and prediabetes in Turkish adults. Eur J Epidemiol 2013;28(2):169-80. [CrossRef]
  • 7. Nabiyev N. Yapay zeka. 4th ed. Ankara; 2012.
  • 8. Buch V, Varughese G, Maruthappu M. Artificial intelligence in diabetes care. Diabet Med 2018;35(4):495-7. [CrossRef]
  • 9. Bulut M. Önsöz. In: Bulut M, Dilmen N, Bora E, Gezer M, Erol Ç, Türker Şener L, editors. Sağlıkta Yapay Zekâ. İstanbul, Turkey: Çağlayan Kitabevi; 2019.
  • 10. Fagherazzi G, Ravaud P. Digital diabetes: Perspectives for diabetes prevention, management and research. Diabetes Metab 2019;45(4):322-9. [CrossRef]
  • 11. Ellahham S. Artificial Intelligence: The Future for Diabetes Care. Am J Med 2020;133(8):895-900. [CrossRef]
  • 12. Huang Y, McCullagh P, Black N, Harper R. Feature selection and classification model construction on type 2 diabetic patients’ data. Artif Intell Med 2007;41(3):251-62. [CrossRef]
  • 13. Makino M, Yoshimoto R, Ono M, Itoko T, Katsuki T, Koseki A, et al. Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning. Sci Rep 2019;9(11862):1-9. [CrossRef]
  • 14. Özmen EP, Özcan T. Diagnosis of diabetes mellitus using artificial neural network and classification and regression tree optimized with genetic algorithm. J Forecast 2020;39(4):661-70. [CrossRef]
  • 15. Wong TY, Sabanayagam C. Strategies to tackle the global burden of diabetic retinopathy: from epidemiology to artificial intelligence. Ophthalmologica 2020;243(1):9-20. [CrossRef]
  • 16. Zhao M, Jiang Y. Great expectations and challenges of artificial intelligence in the screening of diabetic retinopathy. Eye 2020;34(3):418-9. [CrossRef]
  • 17. Contreras I, Vehi J. Artificial intelligence for diabetes management and decision support: literature review. J Med Internet Res 2018;20(5):e10775. [CrossRef]
  • 18. Koçoğlu F. “Endüstri 4.0” Konusu Üzerine R Programlama Dili İle Bibliyometrik Analiz. In: Başal, Handan Asude, Ulutürk Y, Öner NK, editors. Modern dönemde edebiyat, eğitim, iktisat ve mühendislik. Ankara: Berikan Yayınevi; 2018. p. 859-89.
  • 19. TÜBİTAK ULAKBİM, Cahit Arf Bilgi Merkezi. Bibliyometrik Analiz Sıkça Sorulan Sorular. [cited 2020 Aug 27]. Available from: https://cabim.ulakbim.gov.tr/bibliyometrik-analiz/ bibliyometrik-analiz-sikca-sorulan-sorular/
  • 20. Ellegaard O, Wallin JA. The bibliometric analysis of scholarly production: How great is the impact? Scientometrics 2015;105(3):1809-31. [CrossRef]
  • 21. Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. How to conduct a bibliometric analysis: An overview and guidelines. J Bus Res 2021;133:285-96. [CrossRef]
  • 22. Baas J, Schotten M, Plume A, Cote G, Karimi R. Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quant Sci Stud 2020;1(1):377-86. [CrossRef]
  • 23. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2018. Available from: https://www.R-project. org/
  • 24. RStudio Team. RStudio: Integrated Development Environment for R Boston, MA: RStudio, PBC; 2020. Available from: http://www.rstudio.com/
  • 25. Aria M, Cuccurullo C. bibliometrix: An R-tool for comprehensive science mapping analysis. J Informetr 2017;11(4):959-75. [CrossRef]
  • 26. Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, et al. Welcome to the Tidyverse. J Open Source Softw 2019;4(43):1686. [CrossRef]
  • 27. Elango B, Rajendran P. Authorship trends and collaboration pattern in the marine sciences literature : a scientometric study. Int J Inf Dissem Technol 2012;2(3):166-9.
  • 28. Koseoglu MA. Mapping the institutional collaboration network of strategic management research: 1980-2014. Scientometrics 2016;109(1):203-26. [CrossRef]
  • 29. Kaynak O. The golden age of Artificial Intelligence. Discov Artif Intell 2021;1(1). [CrossRef]
  • 30. Agartan TI. Politics of success stories in the path towards Universal Health Coverage: The case of Turkey. Dev Policy Rev 2021;39(2):283-302. [CrossRef]
  • 31. Buse C, Unluonen, K. Economic evaluation of health tourism in Turkey. Journal of Tourismology 2020;6(1):99-109. [CrossRef]
Toplam 31 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Sağlık Kurumları Yönetimi
Bölüm ARAŞTIRMA
Yazarlar

Denizhan Demirkol 0000-0001-7343-9610

Fatma Önay Koçoğlu 0000-0002-1096-9865

Şamil Aktaş 0000-0002-9242-3179

Çiğdem Erol 0000-0002-5057-7145

Proje Numarası 37697
Yayımlanma Tarihi 24 Mart 2022
Gönderilme Tarihi 26 Nisan 2021
Yayımlandığı Sayı Yıl 2022 Cilt: 85 Sayı: 2

Kaynak Göster

APA Demirkol, D., Koçoğlu, F. Ö., Aktaş, Ş., Erol, Ç. (2022). A BIBLIOMETRIC ANALYSIS OF THE RELATIONSHIP BETWEEN DIABETES AND ARTIFICIAL INTELLIGENCE. Journal of Istanbul Faculty of Medicine, 85(2), 249-257. https://doi.org/10.26650/IUITFD.928111
AMA Demirkol D, Koçoğlu FÖ, Aktaş Ş, Erol Ç. A BIBLIOMETRIC ANALYSIS OF THE RELATIONSHIP BETWEEN DIABETES AND ARTIFICIAL INTELLIGENCE. İst Tıp Fak Derg. Mart 2022;85(2):249-257. doi:10.26650/IUITFD.928111
Chicago Demirkol, Denizhan, Fatma Önay Koçoğlu, Şamil Aktaş, ve Çiğdem Erol. “A BIBLIOMETRIC ANALYSIS OF THE RELATIONSHIP BETWEEN DIABETES AND ARTIFICIAL INTELLIGENCE”. Journal of Istanbul Faculty of Medicine 85, sy. 2 (Mart 2022): 249-57. https://doi.org/10.26650/IUITFD.928111.
EndNote Demirkol D, Koçoğlu FÖ, Aktaş Ş, Erol Ç (01 Mart 2022) A BIBLIOMETRIC ANALYSIS OF THE RELATIONSHIP BETWEEN DIABETES AND ARTIFICIAL INTELLIGENCE. Journal of Istanbul Faculty of Medicine 85 2 249–257.
IEEE D. Demirkol, F. Ö. Koçoğlu, Ş. Aktaş, ve Ç. Erol, “A BIBLIOMETRIC ANALYSIS OF THE RELATIONSHIP BETWEEN DIABETES AND ARTIFICIAL INTELLIGENCE”, İst Tıp Fak Derg, c. 85, sy. 2, ss. 249–257, 2022, doi: 10.26650/IUITFD.928111.
ISNAD Demirkol, Denizhan vd. “A BIBLIOMETRIC ANALYSIS OF THE RELATIONSHIP BETWEEN DIABETES AND ARTIFICIAL INTELLIGENCE”. Journal of Istanbul Faculty of Medicine 85/2 (Mart 2022), 249-257. https://doi.org/10.26650/IUITFD.928111.
JAMA Demirkol D, Koçoğlu FÖ, Aktaş Ş, Erol Ç. A BIBLIOMETRIC ANALYSIS OF THE RELATIONSHIP BETWEEN DIABETES AND ARTIFICIAL INTELLIGENCE. İst Tıp Fak Derg. 2022;85:249–257.
MLA Demirkol, Denizhan vd. “A BIBLIOMETRIC ANALYSIS OF THE RELATIONSHIP BETWEEN DIABETES AND ARTIFICIAL INTELLIGENCE”. Journal of Istanbul Faculty of Medicine, c. 85, sy. 2, 2022, ss. 249-57, doi:10.26650/IUITFD.928111.
Vancouver Demirkol D, Koçoğlu FÖ, Aktaş Ş, Erol Ç. A BIBLIOMETRIC ANALYSIS OF THE RELATIONSHIP BETWEEN DIABETES AND ARTIFICIAL INTELLIGENCE. İst Tıp Fak Derg. 2022;85(2):249-57.

Contact information and address

Addressi: İ.Ü. İstanbul Tıp Fakültesi Dekanlığı, Turgut Özal Cad. 34093 Çapa, Fatih, İstanbul, TÜRKİYE

Email: itfdergisi@istanbul.edu.tr

Phone: +90 212 414 21 61