Mapping the landscape of machine learning in chronic disease management: A comprehensive bibliometric study
Objective This study aims to reveal global advancements and trends in machine learning (ML) for chronic disease management through a comprehensive bibliometric analysis, identifying research priorities to guide deeper exploration in the future. Methods Relevant documents on ML and chronic disease ma...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-07-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251361614 |
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| author | Shiying Shen Wenhao Qi Sixie Li Jianwen Zeng Xin Liu Xiaohong Zhu Chaoqun Dong Bin Wang Qian Xu Shihua Cao |
| author_facet | Shiying Shen Wenhao Qi Sixie Li Jianwen Zeng Xin Liu Xiaohong Zhu Chaoqun Dong Bin Wang Qian Xu Shihua Cao |
| author_sort | Shiying Shen |
| collection | DOAJ |
| description | Objective This study aims to reveal global advancements and trends in machine learning (ML) for chronic disease management through a comprehensive bibliometric analysis, identifying research priorities to guide deeper exploration in the future. Methods Relevant documents on ML and chronic disease management were retrieved from the core Web of Science database. Visual analyses of publication volume, research institutions, and countries were conducted using CiteSpace, VOSviewer, RStudio, and other software. An expert panel further analyzed the scale, trends, and potential connections between various ML algorithms and chronic diseases. Results A total of 1,242 documents were included in this study. The findings indicate a continuous rise in studies on ML in chronic disease management, with the United States (n = 303, 23.5%) and China (n = 259, 20.1%) as primary research contributors. Logistic regression (n = 459) remains the most widely used algorithm, while neural networks (n = 183) show promising potential. Research hotspots are concentrated in diabetes and cardiovascular disease, focusing mainly on risk prediction, disease diagnosis, and personalized treatment. Conclusion ML is rapidly integrating into personalized medicine, real-time monitoring, and multimodal data fusion. However, challenges such as limited collaboration, weak model generalization, and data privacy persist. Future efforts should prioritize algorithm optimization and multisource data integration to advance clinical applications. |
| format | Article |
| id | doaj-art-7429a5ef994d4fbbaedd608f7212cab2 |
| institution | DOAJ |
| issn | 2055-2076 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-7429a5ef994d4fbbaedd608f7212cab22025-08-20T02:50:53ZengSAGE PublishingDigital Health2055-20762025-07-011110.1177/20552076251361614Mapping the landscape of machine learning in chronic disease management: A comprehensive bibliometric studyShiying Shen0Wenhao Qi1Sixie Li2Jianwen Zeng3Xin Liu4Xiaohong Zhu5Chaoqun Dong6Bin Wang7Qian Xu8Shihua Cao9 School of Nursing, , Hangzhou, China School of Nursing, , Hangzhou, China School of Nursing, , Hangzhou, China School of Nursing, , Hangzhou, China School of Nursing, , Hangzhou, China School of Nursing, , Hangzhou, China School of Nursing, , Hangzhou, China School of Nursing, , Hangzhou, China Department of Nursing, QianDongNan National Polytechnic, Kaili, China Key Laboratory of Cognitive Disorder Assessment Technology, Zhejiang Province, Hangzhou, ChinaObjective This study aims to reveal global advancements and trends in machine learning (ML) for chronic disease management through a comprehensive bibliometric analysis, identifying research priorities to guide deeper exploration in the future. Methods Relevant documents on ML and chronic disease management were retrieved from the core Web of Science database. Visual analyses of publication volume, research institutions, and countries were conducted using CiteSpace, VOSviewer, RStudio, and other software. An expert panel further analyzed the scale, trends, and potential connections between various ML algorithms and chronic diseases. Results A total of 1,242 documents were included in this study. The findings indicate a continuous rise in studies on ML in chronic disease management, with the United States (n = 303, 23.5%) and China (n = 259, 20.1%) as primary research contributors. Logistic regression (n = 459) remains the most widely used algorithm, while neural networks (n = 183) show promising potential. Research hotspots are concentrated in diabetes and cardiovascular disease, focusing mainly on risk prediction, disease diagnosis, and personalized treatment. Conclusion ML is rapidly integrating into personalized medicine, real-time monitoring, and multimodal data fusion. However, challenges such as limited collaboration, weak model generalization, and data privacy persist. Future efforts should prioritize algorithm optimization and multisource data integration to advance clinical applications.https://doi.org/10.1177/20552076251361614 |
| spellingShingle | Shiying Shen Wenhao Qi Sixie Li Jianwen Zeng Xin Liu Xiaohong Zhu Chaoqun Dong Bin Wang Qian Xu Shihua Cao Mapping the landscape of machine learning in chronic disease management: A comprehensive bibliometric study Digital Health |
| title | Mapping the landscape of machine learning in chronic disease management: A comprehensive bibliometric study |
| title_full | Mapping the landscape of machine learning in chronic disease management: A comprehensive bibliometric study |
| title_fullStr | Mapping the landscape of machine learning in chronic disease management: A comprehensive bibliometric study |
| title_full_unstemmed | Mapping the landscape of machine learning in chronic disease management: A comprehensive bibliometric study |
| title_short | Mapping the landscape of machine learning in chronic disease management: A comprehensive bibliometric study |
| title_sort | mapping the landscape of machine learning in chronic disease management a comprehensive bibliometric study |
| url | https://doi.org/10.1177/20552076251361614 |
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