โก Quick Summary
This study conducted a comprehensive bibliometric analysis of machine learning (ML) applications in the field of diabetes, revealing a steady increase in publications over the past 14 years. The findings highlight the USA as the leading contributor, with significant insights into key research areas such as prediction models and diabetic retinopathy.
๐ Key Details
- ๐ Dataset: 5,222 publications from January 1, 2010, to December 31, 2023
- ๐ Leading countries: USA, China, India
- ๐งโ๐ฌ Most prolific authors: Tien Yin Wong (22 articles), Pantelis Georgiou (20 articles), Pau Herrero (19 articles)
- ๐ Key research areas: Machine learning, prediction models, diabetic retinopathy, deep learning, diagnostics
๐ Key Takeaways
- ๐ Publication trend: Continuous growth in ML research related to diabetes over 14 years.
- ๐บ๐ธ USA emerged as the most active country in this research domain.
- ๐ค International collaboration: The USA showed the highest level of cooperation with other countries.
- ๐จโ๐ฌ Tien Yin Wong is the leading author, contributing significantly to the field.
- ๐ฌ Key areas of focus: Include prediction models and diagnostics for diabetic complications.
- ๐ Bibliometric tools: VOSviewer and Microsoft Excel were used for data visualization.
- ๐ก Implications: Findings can guide policymakers and practitioners in decision-making.
- ๐ฐ Budgeting: Results can inform government funding for targeted diabetes research.
๐ Background
The integration of machine learning into healthcare has the potential to transform the management of chronic diseases, particularly diabetes. As diabetes continues to be a global health challenge, understanding the role of ML in this context is crucial for improving patient outcomes and healthcare efficiency.
๐๏ธ Study
This study utilized a bibliometric approach to analyze the landscape of ML research in diabetes, drawing data from the Web of Science Core Collection (WoSCC). The analysis covered a period from 2010 to 2023, aiming to map the evolution of research trends and identify key contributors in the field.
๐ Results
The analysis revealed a total of 5,222 publications related to ML in diabetes, with a notable increase in the number of studies published each year. The USA led in both the volume of research and international collaboration, indicating a robust research environment. Key areas of research included prediction models and diagnostics, particularly concerning diabetic retinopathy.
๐ Impact and Implications
The findings from this study serve as a valuable resource for researchers, policymakers, and healthcare practitioners. By highlighting the trends and key contributors in ML research for diabetes, this analysis can guide future research directions and funding allocations, ultimately aiming to enhance diabetes management and patient care.
๐ฎ Conclusion
This bibliometric analysis underscores the significant role of machine learning in advancing diabetes research. As the field continues to grow, it is essential for stakeholders to leverage these insights to foster collaboration and innovation, paving the way for improved healthcare solutions. The future of diabetes management looks promising with the integration of ML technologies!
๐ฌ Your comments
What are your thoughts on the impact of machine learning in diabetes research? We would love to hear your insights! ๐ฌ Share your comments below or connect with us on social media:
Analysis and Mapping of Machine Learning in the Context of Diabetes.
Abstract
BACKGROUND AND AIMS: The application of machine learning (ML) has started to change some important aspects of health care in diabetes. We aimed to utilize a bibliometric approach to analyze and map ML in the context of diabetes.
METHODS: To build our data set, we searched from the Web of Science Core Collection (WoSCC) database, and restricted our search from January 1, 2010 to December 31, 2023. For citation analysis, the online services of WoS were used to investigate the information content of the data set, VOSviewer and Microsoft Excel 2013 were employed to construct and visualize the bibliographic data.
RESULTS: Overall, 5,222 results that met the criteria were retrieved. The trend of published studies indicates that the number of publications has steadily increased over the past 14 years. The most active country was found to be USA, followed by the China and India. The highest level of cooperation with other countries belonged to the USA. The most prolific author on ML in the context of diabetes was Tien Yin Wong, with twenty-two articles affiliated at Tsinghua University; after that, Pantelis Georgiou with twenty articles affiliated at the Imperial College London, and Pau Herrero, with nineteen articles affiliated at Tijuana Institute of Technology. The most prolific research areas were machine learning, prediction models, diabetic retinopathy, deep learning, and diagnostics.
CONCLUSION: The results of this study are a rich scientific source of ML for diabetes to guide researchers. This study can guide policymakers, physicians, and practitioners to help in the decision-making process. In addition, the findings will be useful for governments to guide future budgets for target studies.
Author: [‘Ghamgosar A’, ‘Nemati-Anaraki L’, ‘Zarghani M’, ‘Bazri H’, ‘Ahmadian L’, ‘Galavi Z’, ‘Norouzi S’]
Journal: Health Sci Rep
Citation: Ghamgosar A, et al. Analysis and Mapping of Machine Learning in the Context of Diabetes. Analysis and Mapping of Machine Learning in the Context of Diabetes. 2025; 8:e71167. doi: 10.1002/hsr2.71167